
# import all necessary packages
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
import datetime
import math

%matplotlib inline


# import data and show the head rows
df = pd.read_csv('201902-fordgobike-tripdata.csv')
df.head()


# check the structure of the dataset
df.shape

(183412, 16)


# check the data types
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 183412 entries, 0 to 183411
Data columns (total 16 columns):
 #   Column                   Non-Null Count   Dtype  
---  ------                   --------------   -----  
 0   duration_sec             183412 non-null  int64  
 1   start_time               183412 non-null  object 
 2   end_time                 183412 non-null  object 
 3   start_station_id         183215 non-null  float64
 4   start_station_name       183215 non-null  object 
 5   start_station_latitude   183412 non-null  float64
 6   start_station_longitude  183412 non-null  float64
 7   end_station_id           183215 non-null  float64
 8   end_station_name         183215 non-null  object 
 9   end_station_latitude     183412 non-null  float64
 10  end_station_longitude    183412 non-null  float64
 11  bike_id                  183412 non-null  int64  
 12  user_type                183412 non-null  object 
 13  member_birth_year        175147 non-null  float64
 14  member_gender            175147 non-null  object 
 15  bike_share_for_all_trip  183412 non-null  object 
dtypes: float64(7), int64(2), object(7)
memory usage: 22.4+ MB


# check duplicates
df.duplicated().sum()

0


# check missing values
print('Total Missing Values:', df.isnull().sum().sum())
df.isnull().sum()

Total Missing Values: 17318

duration_sec                  0
start_time                    0
end_time                      0
start_station_id            197
start_station_name          197
start_station_latitude        0
start_station_longitude       0
end_station_id              197
end_station_name            197
end_station_latitude          0
end_station_longitude         0
bike_id                       0
user_type                     0
member_birth_year          8265
member_gender              8265
bike_share_for_all_trip       0
dtype: int64


df.describe()


# dropping columns: 'start_station_id' and 'end_station_id'
df.drop(['start_station_id', 'end_station_id'], axis = 1, inplace = True)


# converting incorrect data types to correct ones
# start_time and end_time: From Object To Datetime
df['start_time'] = pd.to_datetime(df['start_time'])
df['end_time'] = pd.to_datetime(df['end_time'])

# user_type, member_gender and bike_share_for_all_trip: From Object To Category
df['user_type'] = df['user_type'].astype('category')
df['bike_share_for_all_trip'] = df['bike_share_for_all_trip'].astype('category')


# imputing and filling up missing values
# filling up missing values in 'start_station_name', 'end_station_name' and 'member_gender' with 'Not Defined'
df.start_station_name.fillna("Not_Defined", inplace = True)
df.end_station_name.fillna("Not_Defined", inplace = True)

# filling up missing values in 'member_birth_year' with '0'
df.member_birth_year.fillna(0, inplace = True)


# Imputing categorical variable needs to fill NaN values in 'member_gender' with 'Not_Defined' 
# and store it as string first then converting it to category
df['member_gender'].fillna("Not_Defined", inplace = True)
# converting 'member_gender' to category
df['member_gender'] = df['member_gender'].astype('category')


# confirm changes are applied
df.isnull().sum()

duration_sec               0
start_time                 0
end_time                   0
start_station_name         0
start_station_latitude     0
start_station_longitude    0
end_station_name           0
end_station_latitude       0
end_station_longitude      0
bike_id                    0
user_type                  0
member_birth_year          0
member_gender              0
bike_share_for_all_trip    0
dtype: int64


# extracting duration in minutes and rounding to decimal of 2 points
df['duration_minute'] = df['duration_sec']/60
df['duration_minute'] = df['duration_minute'].round(2)
# extracting duration in hour and rounding to decimal of 2 points
df['duration_hour'] = df['duration_minute']/60
df['duration_hour'] = df['duration_hour'].round(2)


# extracting start and end time in day hour, day, weekday and month
# extracting start and end time in day hour
df['start_time_hour'] = df.start_time.dt.strftime('%H').astype('int')
df['end_time_hour'] = df.end_time.dt.strftime('%H').astype('int')

# extracting start and end time in day
df['start_time_day'] = df.start_time.dt.day
df['end_time_day'] = df.end_time.dt.day

# extracting start and end  time in weekday
df['start_time_weekday'] = df.start_time.dt.strftime('%A')
df['end_time_weekday'] = df.end_time.dt.strftime('%A')

# extracting start and end time in month
df['start_time_month'] = df.start_time.dt.strftime('%B')
df['end_time_month'] = df.end_time.dt.strftime('%B')


# extracting members age and replacing age values with 2019 # which previously imputed with 0 value
# in place of NaN values and finaly converting age to int datatype
df['member_age'] = 2019 - df['member_birth_year']
df['member_age'] = df['member_age'].replace(to_replace = 2019, value = 0).astype('int64')


# Use Haversine formula coded by Wayne Dyck to calculate distance between latitude-longitude pairs with Python

import math
from math import radians, sin, cos, acos

def distance(origin, destination):
    lat1, lon1 = origin
    lat2, lon2 = destination
    radius = 6371 # km

    dlat = math.radians(lat2-lat1)
    dlon = math.radians(lon2-lon1)
    a = math.sin(dlat/2) * math.sin(dlat/2) + math.cos(math.radians(lat1)) \
        * math.cos(math.radians(lat2)) * math.sin(dlon/2) * math.sin(dlon/2)
    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))
    d = radius * c

    return d


# extracting distance in kilometers
df['distance_km'] = df.apply(lambda x: distance((x['start_station_latitude'], x['start_station_longitude']),
                                                (x['end_station_latitude'], x['end_station_longitude'])), axis=1)


# check the final structure of the dataset after wrangling the data
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 183412 entries, 0 to 183411
Data columns (total 26 columns):
 #   Column                   Non-Null Count   Dtype         
---  ------                   --------------   -----         
 0   duration_sec             183412 non-null  int64         
 1   start_time               183412 non-null  datetime64[ns]
 2   end_time                 183412 non-null  datetime64[ns]
 3   start_station_name       183412 non-null  object        
 4   start_station_latitude   183412 non-null  float64       
 5   start_station_longitude  183412 non-null  float64       
 6   end_station_name         183412 non-null  object        
 7   end_station_latitude     183412 non-null  float64       
 8   end_station_longitude    183412 non-null  float64       
 9   bike_id                  183412 non-null  int64         
 10  user_type                183412 non-null  category      
 11  member_birth_year        183412 non-null  float64       
 12  member_gender            183412 non-null  category      
 13  bike_share_for_all_trip  183412 non-null  category      
 14  duration_minute          183412 non-null  float64       
 15  duration_hour            183412 non-null  float64       
 16  start_time_hour          183412 non-null  int32         
 17  end_time_hour            183412 non-null  int32         
 18  start_time_day           183412 non-null  int64         
 19  end_time_day             183412 non-null  int64         
 20  start_time_weekday       183412 non-null  object        
 21  end_time_weekday         183412 non-null  object        
 22  start_time_month         183412 non-null  object        
 23  end_time_month           183412 non-null  object        
 24  member_age               183412 non-null  int64         
 25  distance_km              183412 non-null  float64       
dtypes: category(3), datetime64[ns](2), float64(8), int32(2), int64(5), object(6)
memory usage: 31.3+ MB


# check duration in seconds summary statistics and summary statistics to a scale of log10
(df.duration_sec.describe(), np.log10(df['duration_sec'].describe()))

(count    183412.000000
 mean        726.078435
 std        1794.389780
 min          61.000000
 25%         325.000000
 50%         514.000000
 75%         796.000000
 max       85444.000000
 Name: duration_sec, dtype: float64,
 count    5.263428
 mean     2.860984
 std      3.253917
 min      1.785330
 25%      2.511883
 50%      2.710963
 75%      2.900913
 max      4.931682
 Name: duration_sec, dtype: float64)


# plotting Duration in seconds
# figure size and color palette
base_color = sb.color_palette()[0]
plt.figure(figsize =[20, 6])

# HISTOGRAM ON LEFT: full data without scaling
plt.subplot(1,2, 1)
plt.hist(data = df, x = 'duration_sec')
plt.xlabel('Duration/Second without Axis Scaling')
plt.ylabel('Counts of Rides')
plt.title("Non-scaled X-axis Duration per Second", y=1.05, fontsize=14, fontweight='bold');


# HISTOGRAM ON RIGHT: data with x axis limits 
plt.subplot(1,2, 2)
plt.hist(data = df, x = 'duration_sec')
# x-axis scale limit
plt.xlim(0,20000)
plt.xlabel('Duration/Second with Axis Scaling')
plt.ylabel('Counts of Rides')
plt.title("Scaled X-axis Duration per Second up to 20k seconds", y=1.05, fontsize=14, fontweight='bold');


# Axis transformation: x axis scaled and transformed to log-type for Duration in seconds
# Bin size
bins = 10 ** np.arange(1, 5+0.025, 0.025)
# Generate the x-ticks you want to apply
ticks = [1, 3, 10, 30, 100, 200, 300, 500,700, 1000, 2000, 3000, 5000, 10000, 30000, 100000]
# Convert ticks into string values, to be displaye dlong the x-axis
labels = ['{}'.format(v) for v in ticks]
# Plot the histogram
plt.figure(figsize =[15, 6])
plt.hist(data=df, x='duration_sec', bins=bins)
# The argument in the xscale() represents the axis scale type to apply.
plt.xscale('log')
# Apply x-ticks
plt.xticks(ticks, labels)
# labels and titles
plt.xlabel('Duration/Second')
plt.ylabel('Counts of Rides')
plt.title("Transformed Log-type Duration/Second Distribution", y=1.05, fontsize=14, fontweight='bold');


# find 95% statistic of duration per second 
df['duration_sec'].describe(percentiles=[.95])

count    183412.000000
mean        726.078435
std        1794.389780
min          61.000000
50%         514.000000
95%        1571.000000
max       85444.000000
Name: duration_sec, dtype: float64


# check duration in minutes summary statistics and same summary statistics to a scale of log10
df.duration_minute.describe(), np.log10(df['duration_minute'].describe())

(count    183412.000000
 mean         12.101301
 std          29.906501
 min           1.020000
 25%           5.420000
 50%           8.570000
 75%          13.270000
 max        1424.070000
 Name: duration_minute, dtype: float64,
 count    5.263428
 mean     1.082832
 std      1.475766
 min      0.008600
 25%      0.733999
 50%      0.932981
 75%      1.122871
 max      3.153531
 Name: duration_minute, dtype: float64)


# plotting duration in minutes
# figure size and color palette
base_color = sb.color_palette()[0]
plt.figure(figsize =[20, 6])

# HISTOGRAM ON LEFT: Duration/Minute without Axis Scaling
plt.subplot(1,2, 1)
plt.hist(data = df, x = 'duration_minute')
# labels and titles
plt.xlabel('Duration/Minute without Axis Scaling')
plt.ylabel('Counts of Rides')
plt.title("Non-scaled X-axis Duration per Minute", y=1.05, fontsize=14, fontweight='bold');

# HISTOGRAM ON RIGHT: Duration/Minute with Axis Scaling 
plt.subplot(1,2, 2)
plt.hist(data = df, x = 'duration_minute')
# x-axis scale limit
plt.xlim(0,300)
# labels and titles
plt.xlabel('Duration/Minute with Axis Scaling')
plt.ylabel('Counts of Rides')
plt.title("Scaled X-axis Duration per Minute up to 300 minutes", y=1.05, fontsize=14, fontweight='bold');


# Axis transformation: x axis scaled and transformed to log-type for duration/minute
# Bin size
bins = 10 ** np.arange(0, 3.5+0.025, 0.025)
# Generate the x-ticks you want to apply
ticks = [1, 2, 3, 5, 10, 20, 30, 50, 100, 200, 300, 500, 1000, 2000]
# Convert ticks into string values, to be displaye dlong the x-axis
labels = ['{}'.format(v) for v in ticks]
# Plot the histogram
plt.figure(figsize =[15, 6])
plt.hist(data=df, x='duration_minute', bins=bins)
# The argument in the xscale() represents the axis scale type to apply.
plt.xscale('log')
# Apply x-ticks
plt.xticks(ticks, labels)
# labels and titles
plt.xlabel('Duration/Minute')
plt.ylabel('Counts of Rides')
plt.title("Transformed Log-type Duration/Minute Distribution", y=1.05, fontsize=14, fontweight='bold');


# find 95% statistic of duration per minute 
df['duration_minute'].describe(percentiles=[.95])

count    183412.000000
mean         12.101301
std          29.906501
min           1.020000
50%           8.570000
95%          26.180000
max        1424.070000
Name: duration_minute, dtype: float64


# check duration in hour summary statistics and summary statistics to a scale of log10
(((df.duration_hour) * 10).describe(), np.log10(df['duration_hour'].describe()))

(count    183412.000000
 mean          2.016845
 std           4.984551
 min           0.200000
 25%           0.900000
 50%           1.400000
 75%           2.200000
 max         237.300000
 Name: duration_hour, dtype: float64,
 count    5.263428
 mean    -0.695328
 std     -0.302374
 min     -1.698970
 25%     -1.045757
 50%     -0.853872
 75%     -0.657577
 max      1.375298
 Name: duration_hour, dtype: float64)


# plotting duration in hour
# figure size and color palette
base_color = sb.color_palette()[0]
plt.figure(figsize =[20, 6])

# HISTOGRAM ON LEFT: Duration/Hour without Axis Scaling
plt.subplot(1,2, 1)
plt.hist(data = df, x = 'duration_hour')
# labels and titles
plt.xlabel('Duration/Hour without Axis Scaling')
plt.ylabel('Counts of Rides')
plt.title("Non-scaled X-axis Duration per Hour", y=1.05, fontsize=14, fontweight='bold');

# HISTOGRAM ON RIGHT: Scaled Duration/Hour up to 5 hours
plt.subplot(1,2, 2)
plt.hist(data = df, x = 'duration_hour')
# x-axis scale limit
plt.xlim(0,5)
# labels and titles
plt.xlabel('Scaled Duration/Hour up to 5 hours')
plt.ylabel('Counts of Rides')
plt.title("Scaled X-axis Duration per Hour", y=1.05, fontsize=14, fontweight='bold');


# Axis transformation: x axis scaled and transformed to log-type for duration/hour
# Bin size
bins = 10 ** np.arange(0, 1.3+0.025, 0.025)
# Generate the x-ticks you want to apply
ticks = [1, 2, 5, 10, 15, 20, 25]
# Convert ticks into string values, to be displaye dlong the x-axis
labels = ['{}'.format(v) for v in ticks]
# Plot the histogram
plt.figure(figsize =[15, 6])
plt.hist(data=df, x='duration_hour', bins=bins)
# The argument in the xscale() represents the axis scale type to apply.
plt.xscale('log')
# Apply x-ticks
plt.xticks(ticks, labels)
# labels and titles
plt.xlabel('Duration/Hour')
plt.ylabel('Counts of Rides')
plt.title("Duration per Hour Distribution ", y=1.05, fontsize=14, fontweight='bold');


# find 95% statistic of duration per hour 
(df['duration_hour']*10).describe(percentiles=[.95])

count    183412.000000
mean          2.016845
std           4.984551
min           0.200000
50%           1.400000
95%           4.400000
max         237.300000
Name: duration_hour, dtype: float64


# plotting start and end time hour
# figure size and color palette
plt.figure(figsize =[15, 6])
base_color = sb.color_palette()[0]

# BAR CHART ON LEFT: Trip Sart Hour
plt.subplot(1,2, 1)
# Bar chart for Trip Start Hour
plt.hist(data=df, x='start_time_hour', color=base_color)
# labels and titles
plt.xlabel('Start Hour')
plt.ylabel('Counts of Rides')
plt.title("Trip Sart Hour Distribution", y=1.05, fontsize=14, fontweight='bold');

# BAR CHART ON LEFT: Trip End Hour
plt.subplot(1,2, 2)
# Bar chart for Trip Start Hour
plt.hist(data=df, x='end_time_hour', color=base_color)
# labels and titles
plt.xlabel('End Hour')
plt.ylabel('Counts of Rides')
plt.title("Trip End Hour Distribution", y=1.05, fontsize=14, fontweight='bold');


# check startand end time hour summary statistics
df.start_time_hour.describe(), df.end_time_hour.describe()

(count    183412.000000
 mean         13.458421
 std           4.724978
 min           0.000000
 25%           9.000000
 50%          14.000000
 75%          17.000000
 max          23.000000
 Name: start_time_hour, dtype: float64,
 count    183412.000000
 mean         13.614769
 std           4.739638
 min           0.000000
 25%           9.000000
 50%          14.000000
 75%          18.000000
 max          23.000000
 Name: end_time_hour, dtype: float64)


# transform start and end time hour summary statistics to a scale of log10
np.log10(df['start_time_hour'].describe()), np.log10(df['end_time_hour'].describe())

C:\Users\sherif\anaconda3\lib\site-packages\pandas\core\series.py:726: RuntimeWarning: divide by zero encountered in log10
  result = getattr(ufunc, method)(*inputs, **kwargs)

(count    5.263428
 mean     1.128994
 std      0.674400
 min          -inf
 25%      0.954243
 50%      1.146128
 75%      1.230449
 max      1.361728
 Name: start_time_hour, dtype: float64,
 count    5.263428
 mean     1.134010
 std      0.675745
 min          -inf
 25%      0.954243
 50%      1.146128
 75%      1.255273
 max      1.361728
 Name: end_time_hour, dtype: float64)


# Axis transformation: x axis scaled and transformed to log-type for start and end time hour
# figure size and color palette
base_color = sb.color_palette()[0]
plt.figure(figsize =[20, 6])

# HISTOGRAM ON LEFT: Trip Start Time Hour
plt.subplot(1,2, 1)
# Axis transformation: x axis scaled and transformed to log-type
# Bin size
bins = 10 ** np.arange(0, 1.4+0.025, 0.05)
# Generate the x-ticks you want to apply
ticks = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23]
# Convert ticks into string values, to be displaye dlong the x-axis
labels = ['{}'.format(v) for v in ticks]
# Plot the histogram
plt.hist(data=df, x='start_time_hour', bins=bins)
# The argument in the xscale() represents the axis scale type to apply.
plt.xscale('log')
# Apply x-ticks
plt.xticks(ticks, labels)
# labels and titles
plt.xlabel('Start Hour')
plt.ylabel('Counts of Rides')
plt.title(" Transformed Trip Start Time Hour Distibution to log-type", y=1.05, fontsize=14, fontweight='bold');


# HISTOGRAM ON RIGHT: Trip End Time Hour
plt.subplot(1,2, 2)
# Axis transformation: x axis scaled and transformed to log-type
# Bin size
bins = 10 ** np.arange(0, 1.4+0.025, 0.05)

# Generate the x-ticks you want to apply
ticks = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23]
# Convert ticks into string values, to be displaye dlong the x-axis
labels = ['{}'.format(v) for v in ticks]
# Plot the histogram
plt.hist(data=df, x='end_time_hour', bins=bins)
# The argument in the xscale() represents the axis scale type to apply.
plt.xscale('log')
# Apply x-ticks
plt.xticks(ticks, labels)

# labels and titles
plt.xlabel('End Hour')
plt.ylabel('Counts of Rides')
plt.title("Transformed Trip End Time Hour Distibution to log-type", y=1.05, fontsize=14, fontweight='bold');


# find summary statistics of start and end time hour
df['start_time_hour'].describe(percentiles=[1]), df['end_time_hour'].describe(percentiles=[1])

(count    183412.000000
 mean         13.458421
 std           4.724978
 min           0.000000
 50%          14.000000
 100%         23.000000
 max          23.000000
 Name: start_time_hour, dtype: float64,
 count    183412.000000
 mean         13.614769
 std           4.739638
 min           0.000000
 50%          14.000000
 100%         23.000000
 max          23.000000
 Name: end_time_hour, dtype: float64)


# plotting bar charts for start and end time days 
# figure size and color palette
plt.figure(figsize =[15, 6])
base_color = sb.color_palette()[0]

#BAR CHART ON LEFT: Trip Start Day
plt.subplot(1,2, 1)
sb.countplot(data=df, x='start_time_day', color=base_color)
# labels and titles
plt.xlabel('Day')
plt.ylabel('Counts of Rides')
plt.xticks(rotation=0)
plt.title("Trip Start Day Distibution", y=1.05, fontsize=14, fontweight='bold');

# BAR CHART ON RIGHT: Trip End Day
plt.subplot(1,2, 2)
sb.countplot(data=df, x='end_time_day', color=base_color)
# labels and titles
plt.xlabel('Day')
plt.ylabel('Counts of Rides')
plt.xticks(rotation=0)
plt.title("Trip End Day Distibution", y=1.05, fontsize=14, fontweight='bold');


# plotting bar charts for sorted start and end time days 
# figure size and color palette
plt.figure(figsize =[15, 10])
base_color = sb.color_palette()[0]

# BAR CHART ON LEFT: Trip Start Day
plt.subplot(1,2, 1)
# Returns the sum of all not-null values in `start_time_day` column
n_start_day = df['start_time_day'].value_counts().sum()
# Recalculating the day_counts just to have clarity.
start_counts = df['start_time_day'].value_counts()
# Get the unique values of the `start_time_day` column, in the decreasing order of the frequency.
start_order = start_counts.index
# Considering the same chart from the Example 1 above, print the text (proportion) on the bars
sb.countplot(data=df, y='start_time_day', color=base_color, order=start_order)
# labels and titles
plt.xlabel('Counts of Rides')
plt.ylabel('Day')
plt.xticks(rotation=60)
plt.title("Sorted Trip Start Day Distibution with Percent/Day", y=1.05, fontsize=14, fontweight='bold');


# BAR CHART ON RIGHT: Trip End Day
plt.subplot(1,2, 2)
# Returns the sum of all not-null values in `start_time_day` column
n_end_day = df['end_time_day'].value_counts().sum()
# Recalculating the day_counts just to have clarity.
end_counts = df['end_time_day'].value_counts()
# Get the unique values of the `start_time_day` column, in the decreasing order of the frequency.
end_order = end_counts.index
# Considering the same chart from the Example 1 above, print the text (proportion) on the bars
sb.countplot(data=df, y='end_time_day', color=base_color, order=end_order)
# labels and titles
plt.xlabel('Counts of Rides')
plt.ylabel('Day')
plt.xticks(rotation=60)
plt.title("Sorted Trip End Day Distibution with Percent/Day", y=1.05, fontsize=14, fontweight='bold');


# plotting start and end time days
# figure size and color palette
plt.figure(figsize =[15, 6])
base_color = sb.color_palette()[0]

# BAR CHART ON LEFT: Trip Start Day
plt.subplot(1,2, 1)
# Bar chart for Trip Start Day
plt.hist(data=df, x='start_time_day', color=base_color)
# labels and titles
plt.xlabel('Hour')
plt.ylabel('Counts of Rides')
plt.title("Trip Sart Day Distribution", y=1.05, fontsize=14, fontweight='bold');

# BAR CHART ON LEFT: Trip End Day
plt.subplot(1,2, 2)
# Bar chart for Trip Start Day
plt.hist(data=df, x='end_time_day', color=base_color)
# labels and titles
plt.xlabel('Hour')
plt.ylabel('Counts of Rides')
plt.title("Trip End Day Distribution", y=1.05, fontsize=14, fontweight='bold');


# check start time day summary statistics
df.start_time_day.describe(), df.end_time_day.describe()

(count    183412.000000
 mean         15.298977
 std           8.028187
 min           1.000000
 25%           8.000000
 50%          15.000000
 75%          22.000000
 max          28.000000
 Name: start_time_day, dtype: float64,
 count    183412.000000
 mean         15.298279
 std           8.028355
 min           1.000000
 25%           8.000000
 50%          15.000000
 75%          22.000000
 max          28.000000
 Name: end_time_day, dtype: float64)


# transform start and end time day summary statistics to a scale of log10
np.log10(df['start_time_day'].describe()), np.log10(df['end_time_day'].describe())

(count    5.263428
 mean     1.184662
 std      0.904617
 min      0.000000
 25%      0.903090
 50%      1.176091
 75%      1.342423
 max      1.447158
 Name: start_time_day, dtype: float64,
 count    5.263428
 mean     1.184643
 std      0.904627
 min      0.000000
 25%      0.903090
 50%      1.176091
 75%      1.342423
 max      1.447158
 Name: end_time_day, dtype: float64)


# plotting sorted start and end time days
# figure size and color palette
plt.figure(figsize =[15, 6])
base_color = sb.color_palette()[0]

# BAR CHART ON LEFT: Trip Start Day
plt.subplot(1,2, 1)
# Axis transformation: x axis scaled and transformed to log-type for start day
# Bin size
bins = 10 ** np.arange(0, 1.5+0.05, 0.05)
# Generate the x-ticks you want to apply
ticks = [1, 2, 3, 4, 5, 6, 7, 8 , 9, 10 , 12, 13, 15, 17, 19, 22, 25, 28]
# Convert ticks into string values, to be displaye dlong the x-axis
labels = ['{}'.format(v) for v in ticks]
# Plot the histogram
plt.hist(data=df, x='start_time_day', bins=bins)
# The argument in the xscale() represents the axis scale type to apply.
plt.xscale('log')
# Apply x-ticks
plt.xticks(ticks, labels)
# labels and titles
plt.xlabel('Trip Start Day')
plt.ylabel('Counts of Rides')
plt.title("Transformed Trip Start Day Distribution", y=1.05, fontsize=14, fontweight='bold');


# BAR CHART ON RIGHT: Trip End Day
plt.subplot(1,2, 2)
# Axis transformation: x axis scaled and transformed to log-type for end day
# Bin size
bins = 10 ** np.arange(0, 1.5+0.05, 0.05)
# Generate the x-ticks you want to apply
ticks = [1, 2, 3, 4, 5, 6, 7, 8 , 9, 10 , 12, 13, 15, 17, 19, 22, 25, 28]
# Convert ticks into string values, to be displaye dlong the x-axis
labels = ['{}'.format(v) for v in ticks]
# Plot the histogram
plt.hist(data=df, x='end_time_day', bins=bins)
# The argument in the xscale() represents the axis scale type to apply.
plt.xscale('log')
# Apply x-ticks
plt.xticks(ticks, labels)
# labels and titles
plt.xlabel('Trip End Day')
plt.ylabel('Counts of Rides')
plt.title("Transformed Trip End Day Distribution", y=1.05, fontsize=14, fontweight='bold');


# check mean rides /start time day
print(df['start_time_day'].value_counts().mean())
# value counts per each end time day.mean()
print(df['end_time_day'].value_counts().mean())

# find summary statistics of start and end time day
df['start_time_day'].describe(percentiles =[1]),df['start_time_day'].describe(percentiles =[1])

6550.428571428572
6550.428571428572

(count    183412.000000
 mean         15.298977
 std           8.028187
 min           1.000000
 50%          15.000000
 100%         28.000000
 max          28.000000
 Name: start_time_day, dtype: float64,
 count    183412.000000
 mean         15.298977
 std           8.028187
 min           1.000000
 50%          15.000000
 100%         28.000000
 max          28.000000
 Name: start_time_day, dtype: float64)


# plotting bar charts for start and end  time weekdays
# figure size and color palette
plt.figure(figsize =[15, 6])
base_color = sb.color_palette()[0]

#BAR CHART ON LEFT: Start Time Weekday
plt.subplot(1,2, 1)
start_weekday_order = df['start_time_weekday'].value_counts().index
sb.countplot(data=df, x='start_time_weekday', color=base_color, order = start_weekday_order)
# labels and titles
plt.xlabel('Weekday')
plt.ylabel('Counts of Rides')
plt.xticks(rotation=60)
plt.title("Trip Start Weekdays Distribution", y=1.05, fontsize=14, fontweight='bold');

# BAR CHART ON RIGHT: End Time Weekday
plt.subplot(1,2, 2)
end_weekday_order = df['end_time_weekday'].value_counts().index
sb.countplot(data=df, x='end_time_weekday', color=base_color, order = end_weekday_order)
# labels and titles
plt.xlabel('Weekday')
plt.ylabel('Counts of Rides')
plt.xticks(rotation=60)
plt.title("Trip End Weekdays Distribution", y=1.05, fontsize=14, fontweight='bold');


# weekday counts
df[['start_time_weekday', 'end_time_weekday']].value_counts()

start_time_weekday  end_time_weekday
Thursday            Thursday            35142
Tuesday             Tuesday             31780
Wednesday           Wednesday           29598
Friday              Friday              28926
Monday              Monday              26812
Sunday              Sunday              15476
Saturday            Saturday            15358
Thursday            Friday                 55
Friday              Saturday               55
Sunday              Monday                 47
Saturday            Sunday                 47
Wednesday           Thursday               43
Monday              Tuesday                40
Tuesday             Wednesday              33
dtype: int64


# plotting pie charts for start and end  time weekdays
# figure size and color palette
plt.figure(figsize =[15, 6])
base_color = sb.color_palette()[0]

# PIE CHART ON LEFT: Start Time Weekday
plt.subplot(1,2, 1)
start_weekday_sorted = df['start_time_weekday'].value_counts()
plt.pie(start_weekday_sorted, labels = start_weekday_sorted.index, autopct='%1.0f%%', startangle=90, counterclock = False)
plt.title("Trip Start Weekdays Proportion", fontsize=15, y=1.05, fontweight='bold')
plt.axis('square');

# PIE CHART ON LEFT: End Time Weekday
plt.subplot(1,2, 2)
end_weekday_sorted = df['end_time_weekday'].value_counts()
plt.pie(end_weekday_sorted, labels = end_weekday_sorted.index, autopct='%1.0f%%', startangle=90, counterclock = False)
plt.title("Trip End Weekdays Proportion", fontsize=15, y=1.05, fontweight='bold')
plt.axis('square');


# average counts per each start time weekday
start_weekday = df['start_time_weekday'].value_counts()
# average counts per each end time weekday
end_weekday = df['end_time_weekday'].value_counts()

print(df['start_time_weekday'].value_counts().mean())
print(df['end_time_weekday'].value_counts().mean())

# find 95% statistic of start and end time weekday
start_weekday.describe(percentiles=[.95]), end_weekday.describe(percentiles=[.95])

26201.714285714286
26201.714285714286

(count        7.000000
 mean     26201.714286
 std       7777.978073
 min      15405.000000
 50%      28981.000000
 95%      34181.800000
 max      35197.000000
 Name: start_time_weekday, dtype: float64,
 count        7.000000
 mean     26201.714286
 std       7774.019825
 min      15413.000000
 50%      28981.000000
 95%      34175.500000
 max      35185.000000
 Name: end_time_weekday, dtype: float64)


# value counts per each start time month
start_month = df['start_time_month'].value_counts()
# value counts per each end time month
end_month = df['end_time_month'].value_counts()

print('Rides per Start Month: \n {}'.format(start_month))
print()
print('Rides per End Month: \n {}'.format(end_month))

#print(end_month)

Rides per Start Month: 
 February    183412
Name: start_time_month, dtype: int64

Rides per End Month: 
 February    183396
March           16
Name: end_time_month, dtype: int64


# plotting bar charts for start and end  time month
# figure size and color palette
plt.figure(figsize =[15, 6])
base_color = sb.color_palette()[0]

#BAR CHART ON LEFT: Start Time Month
plt.subplot(1,2, 1)
start_month_order = df['start_time_month'].value_counts().index
sb.countplot(data=df, x='start_time_month', color=base_color, order = start_month_order)
# labels and titles
plt.xlabel('Month')
plt.ylabel('Counts of Rides')
plt.xticks(rotation=0)
plt.title("Trip Start Month Distribution", y=1.05, fontsize=14, fontweight='bold');

# BAR CHART ON RIGHT: End Time Month
plt.subplot(1,2, 2)
end_month_order = df['end_time_month'].value_counts().index
sb.countplot(data=df, x='end_time_month', color=base_color, order = end_month_order)
# labels and titles
plt.xlabel('Month')
plt.ylabel('Counts of Rides')
plt.xticks(rotation=0)
plt.title("Trip End Month Distribution", y=1.05, fontsize=14, fontweight='bold');


# plotting pie charts for start and end  time month
# figure size and color palette
plt.figure(figsize =[15, 6])
base_color = sb.color_palette()[0]

# PIE CHART ON LEFT: Start Time Month
plt.subplot(1,2, 1)
start_month_sorted = df['start_time_month'].value_counts()
plt.pie(start_month_sorted, labels = start_month_sorted.index, autopct='%1.0f%%', startangle=90, counterclock = False)
plt.title("Trip Start Month Proportion", fontsize=15, y=1.05, fontweight='bold')
plt.axis('square');

# PIE CHART ON LEFT: End Time Month
plt.subplot(1,2, 2)
end_month_sorted = df['end_time_month'].value_counts()
plt.pie(end_month_sorted, labels = end_month_sorted.index, autopct='%0.2f%%', startangle=90, counterclock = False, explode = (0.3, 0))
plt.title("Trip End Month Proportion", fontsize=15, y=1.05, fontweight='bold')
plt.axis('square');


# check total rides started in February and ended in March
march_rides = df.query("end_time_month == 'March'").value_counts().sum()
print('Total rides started in February and ended in March:{}'.format(march_rides))
df.query("end_time_month == 'March'")

Total rides started in February and ended in March:16


# plotting histogram for start and end stations 
# figure size and color palette
base_color = sb.color_palette()[0]
plt.figure(figsize =[15, 6])

# HISTOGRAM ON LEFT: Cluster area of start stations
plt.subplot(1,2, 1)
start_station = df['start_station_name'].value_counts().hist(grid=False, color=base_color)
# labels and titles
plt.xlabel('Total Rides/Station')
plt.ylabel('Rides/day')
plt.xticks(rotation=0)
plt.title("Start Stations Distribution", y=1.05, fontsize=14, fontweight='bold');

# HISTOGRAM ON RIGHT: Cluster area of end stations
plt.subplot(1,2, 2)
end_station = df['end_station_name'].value_counts().hist(grid=False, color=base_color)
# labels and titles
plt.xlabel('Total Rides/Station')
plt.ylabel('Rides/day')
plt.xticks(rotation=0)
plt.title("Start Stations Distribution", y=1.05, fontsize=14, fontweight='bold');


# check counts of start stations and end stations and counts of total rides
df[['start_station_name', 'end_station_name']].describe()


# check counts summary statistics of start stations and end stations/ avrg. rides per station
start_station_counts = df.start_station_name.value_counts()
end_station_counts = df.end_station_name.value_counts()
(start_station_counts.describe(percentiles =[1]), end_station_counts.describe(percentiles =[1]))

(count     330.000000
 mean      555.793939
 std       580.327064
 min         2.000000
 50%       388.500000
 100%     3904.000000
 max      3904.000000
 Name: start_station_name, dtype: float64,
 count     330.000000
 mean      555.793939
 std       629.439781
 min         5.000000
 50%       370.500000
 100%     4857.000000
 max      4857.000000
 Name: end_station_name, dtype: float64)


# plotting clustering areas of start and end stations 
# create a dataframe for start station and its latitude and longitude
start_cluster = df[['start_station_name', 'start_station_latitude', 'start_station_longitude']].value_counts()

# create a dataframe for end station and its latitude and longitude
end_cluster = df[['end_station_name', 'end_station_latitude', 'end_station_longitude']].value_counts()

# figure size 
plt.figure(figsize =[15, 8])

# SCATTER CHART ON LEFT: Cluster area of start stations
plt.subplot(1,2, 1)
# plot cluster area of start stations 
sb.scatterplot(data = start_cluster, x = "start_station_latitude", y = "start_station_longitude", s = 300)
# labels and titles
plt.xlabel('Start Station Latitude')
plt.ylabel('Start Station Longitude')
plt.xticks(rotation=0)
plt.title("Cluster Areas of Start Stations", y=1.05, fontsize=14, fontweight='bold');

# SCATTER CHART ON RIGHT: Cluster area of end stations
plt.subplot(1,2, 2)
# plot cluster area of end stations 
sb.scatterplot(data = end_cluster, x = "end_station_latitude", y = "end_station_longitude", s = 300)
# labels and titles
plt.xlabel('End Station Latitude')
plt.ylabel('End Station Longitude')
plt.xticks(rotation=0)
plt.title("Cluster Areas of End Stations", y=1.05, fontsize=14, fontweight='bold');


# find coordinates summary statistics to identify the range of coordinates for each city
df.start_station_longitude.describe()

count    183412.000000
mean       -122.352664
std           0.117097
min        -122.453704
25%        -122.412408
50%        -122.398285
75%        -122.286533
max        -121.874119
Name: start_station_longitude, dtype: float64


# create 'San_Fransisco' subset based on start station latitude
San_Fransisco = df.query('start_station_longitude > -122.50000 and start_station_longitude < -122.350000')


# number of total rides handled by San Fransisco stations
San_Fransisco.value_counts().sum()

133708


# confirm results
San_Fransisco['start_station_longitude'].describe()

count    133708.000000
mean       -122.407963
std           0.014050
min        -122.453704
25%        -122.418230
50%        -122.404904
75%        -122.395526
max        -122.385655
Name: start_station_longitude, dtype: float64


# create 'Oakland_Berkeley' subset based on start station latitude
Oakland_Berkeley = df.query('start_station_longitude > -122.350000 and start_station_longitude < -122.100000')


# number of total rides handled by Oakland_Berkeley stations
Oakland_Berkeley.value_counts().sum()

41434


# confirm results
Oakland_Berkeley.start_station_longitude.describe()

count    41434.000000
mean      -122.266391
std          0.011906
min       -122.299676
25%       -122.270582
50%       -122.265896
75%       -122.258764
max       -122.213037
Name: start_station_longitude, dtype: float64


# create 'San_Jose' subset based on start station latitude
San_Jose = df.query('start_station_longitude > -122.100000')


# number of total rides handled by San Jose stations
San_Jose.value_counts().sum()

8270


# confirm results
San_Jose.start_station_longitude.describe()

count    8270.000000
mean     -121.890830
std         0.013038
min      -121.980000
25%      -121.895617
50%      -121.886943
75%      -121.883215
max      -121.874119
Name: start_station_longitude, dtype: float64


# plotting histogram for Start Stations' cities based on their coordinates and ride/stations
# figure size and color palette
base_color = sb.color_palette()[0]
plt.figure(figsize =[15, 6])

# HISTOGRAM ON LEFT: San Fransisco City
plt.subplot(1,3, 1)
San_Fransisco_stations = San_Fransisco['start_station_longitude'].value_counts().hist(grid=False, color=base_color)
# labels and title
plt.xlabel("Number of Stations")
plt.ylabel('Counts of Rides')
plt.title("San Fransisco Stations", y=1.05, fontsize=14, fontweight='bold');

# HISTOGRAM ON MIDDLE: Oakland_Berkeley Cities
plt.subplot(1,3, 2)
Oakland_Berkeley_stations = Oakland_Berkeley['start_station_longitude'].value_counts().hist(grid=False, color=base_color)
# labels and title
plt.xlabel("Number of Stations")
plt.ylabel('Counts of Rides')
plt.title("Oakland_Berkeley Stations", y=1.05, fontsize=14, fontweight='bold');

# HISTOGRAM ON RIGHT: San Jose City
plt.subplot(1,3,3)
San_Jose_stations = San_Jose['start_station_longitude'].value_counts().hist(grid=False, color=base_color)
# labels and title
plt.xlabel("Number of Stations")
plt.ylabel('Counts of Rides')
plt.title("San Jose City Stations", y=1.05, fontsize=14, fontweight='bold');


# check counts summary statistics of start stations and end stations/ avg. rides per each city stations
#San_Fransisco_stations
San_Fransisco_stations_counts = San_Fransisco.start_station_name.value_counts()
print('San_Fransisco Stations Capacity/day:{}'. format(round(San_Fransisco_stations_counts.sum()/28)))
print('San_Fransisco Stations Capacity/station/day:{}'. format(round(San_Fransisco_stations_counts.sum()/156/28)))

#Oakland_Berkeley_stations
Oakland_Berkeley_stations_counts = Oakland_Berkeley.start_station_name.value_counts()
print('Oakland_Berkeley Stations Capacity/day:{}'. format(round(Oakland_Berkeley_stations_counts.sum()/28)))
print('Oakland_Berkeley Capacity/station/day:{}'. format(round(Oakland_Berkeley_stations_counts.sum()/127/28)))

#San_Jose_stations
San_Jose_stations_counts = San_Jose.start_station_name.value_counts()
print('San Jose Stations Capacity/day:{}'. format(round(San_Jose_stations_counts.sum()/28)))
print('San Jose Stations Capacity/station/day:{}'. format(round(San_Jose_stations_counts.sum()/47/28)))

# city  summary statistics 
(San_Fransisco_stations_counts.describe(percentiles =[1]), Oakland_Berkeley_stations_counts.describe(percentiles =[1]),
 San_Jose_stations_counts.describe(percentiles =[1]))

San_Fransisco Stations Capacity/day:4775
San_Fransisco Stations Capacity/station/day:31
Oakland_Berkeley Stations Capacity/day:1480
Oakland_Berkeley Capacity/station/day:12
San Jose Stations Capacity/day:295
San Jose Stations Capacity/station/day:6

(count     156.000000
 mean      857.102564
 std       662.948109
 min         2.000000
 50%       710.000000
 100%     3904.000000
 max      3904.000000
 Name: start_station_name, dtype: float64,
 count     127.000000
 mean      326.251969
 std       321.708139
 min         4.000000
 50%       211.000000
 100%     1796.000000
 max      1796.000000
 Name: start_station_name, dtype: float64,
 count     47.000000
 mean     175.957447
 std      208.625414
 min        4.000000
 50%       94.000000
 100%     950.000000
 max      950.000000
 Name: start_station_name, dtype: float64)


# plotting histogram for ride distances
# figure size and color palette
base_color = sb.color_palette()[0]
plt.figure(figsize =[20, 6])

# HISTOGRAM ON LEFT: Distance/km with without Axis Scaling
plt.subplot(1,2, 1)
plt.hist(data = df, x = 'distance_km')
plt.xlabel('Distance/km with without Axis Scaling')
plt.ylabel('Counts of Rides')
plt.title("Non-scaled X-axis Distance/km Distribution", y=1.05, fontsize=14, fontweight='bold');


# HISTOGRAM ON RIGHT: Distance/km with Axis Scaling
plt.subplot(1,2, 2)
plt.hist(data = df, x = 'distance_km')
# x-axis scale limit
plt.xlim(0,15)
plt.xlabel('Distance/km with Axis Scaling')
plt.ylabel('Counts of Rides')
plt.title("Scaled X-axis Distance/km Distribution up to 15km", y=1.05, fontsize=14, fontweight='bold');


# chech summary statistics and summary statistics to a scale of log10
(df.distance_km.describe(),np.log10(df['distance_km'].describe()))

C:\Users\sherif\anaconda3\lib\site-packages\pandas\core\series.py:726: RuntimeWarning: divide by zero encountered in log10
  result = getattr(ufunc, method)(*inputs, **kwargs)

(count    183412.000000
 mean          1.690659
 std           1.097589
 min           0.000000
 25%           0.910955
 50%           1.430738
 75%           2.225687
 max          69.469241
 Name: distance_km, dtype: float64,
 count    5.263428
 mean     0.228056
 std      0.040440
 min          -inf
 25%     -0.040503
 50%      0.155560
 75%      0.347464
 max      1.841793
 Name: distance_km, dtype: float64)


# Axis transformation: x axis scaled and transformed to log-type for ride distances
# Bin size
bins = 10 ** np.arange(0.01, 1.9+0.03, 0.03)
# Generate the x-ticks you want to apply
ticks = [1, 1.2, 1.4, 1.7, 2, 2.3, 2.6, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 12]
# Convert ticks into string values, to be displayed long the x-axis
labels = ['{}'.format(v) for v in ticks]
# Plot the histogram
plt.figure(figsize =[15, 6])
plt.hist(data=df, x='distance_km', bins=bins)
# The argument in the xscale() represents the axis scale type to apply.
plt.xscale('log')
# Apply x-ticks
plt.xticks(ticks, labels)
# labels and titles
plt.xlabel('Distance/km ')
plt.ylabel('Counts of Rides')
plt.title(" Transformed Distance/km Distribution to log-type", y=1.05, fontsize=14, fontweight='bold');


# find 95% statistic of ride distances
df['distance_km'].describe(percentiles=[.95])

count    183412.000000
mean          1.690659
std           1.097589
min           0.000000
50%           1.430738
95%           3.791153
max          69.469241
Name: distance_km, dtype: float64


# check the number of rides per distance > 5km
dist_km = df[df['distance_km'] > 3.791153]
dist_km.shape[0]

9170


# check the number of bike ids
bike_ids = df['bike_id'].value_counts()
print('Total Number of Bike Ids: {}'.format(bike_ids.size))
print('Number of Unique Bike Ids: {}'.format(df['bike_id'].nunique()))
print('Mean Rides per Bike Id: {}'.format(bike_ids.mean()))

Total Number of Bike Ids: 4646
Number of Unique Bike Ids: 4646
Mean Rides per Bike Id: 39.477399913904435


# check 90%  statistic of bike rides per bike id
bike_ids.describe(percentiles=[.9])

count    4646.000000
mean       39.477400
std        38.055172
min         1.000000
50%        26.000000
90%       100.500000
max       191.000000
Name: bike_id, dtype: float64


# check 90%  statistic of bike rides per bike id
top_ids = bike_ids[bike_ids >=100.5]
print('Top 10% of Bike Ids per Bike Rides: {}'.format(top_ids.size))

Top 10% of Bike Ids per Bike Rides: 465


# check 10%  statistic of bike rides per bike id
bike_ids.describe(percentiles=[.1])

count    4646.000000
mean       39.477400
std        38.055172
min         1.000000
10%         6.000000
50%        26.000000
max       191.000000
Name: bike_id, dtype: float64


# find lower 10% of bikes 
lower_ids = bike_ids[bike_ids <= 6]
print('Lower 10% of Bike Ids per Bike Rides: {}'.format(lower_ids.size))

Lower 10% of Bike Ids per Bike Rides: 565


# chech summary statistics and summary statistics to a scale of log10
(df.bike_id.describe(),np.log10(df['bike_id'].describe()))

(count    183412.000000
 mean       4472.906375
 std        1664.383394
 min          11.000000
 25%        3777.000000
 50%        4958.000000
 75%        5502.000000
 max        6645.000000
 Name: bike_id, dtype: float64,
 count    5.263428
 mean     3.650590
 std      3.221253
 min      1.041393
 25%      3.577147
 50%      3.695307
 75%      3.740521
 max      3.822495
 Name: bike_id, dtype: float64)


# plotting histogram for bike id distribution
# figure size and color palette
base_color = sb.color_palette()[0]
plt.figure(figsize =[20, 6])

# HISTOGRAM ON LEFT: Bike Id with without Axis Scaling
plt.subplot(1,2, 1)
plt.hist(data = df, x = 'bike_id')
plt.xlabel('Bike Id Range with without Axis Scaling')
plt.ylabel('Counts of Rides')
plt.title("Non-scaled X-axis Bike Id Distribution", y=1.05, fontsize=14, fontweight='bold');

# HISTOGRAM ON RIGHT: Bike Id with Axis Scaling
plt.subplot(1,2, 2)
# Axis transformation: x axis scaled and transformed to log-type for bike ids
# Bin size
bins = 10 ** np.arange(1, 3.83+0.1, 0.1)
# Generate the x-ticks you want to apply
ticks = [100, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7000]

# Convert ticks into string values, to be displayed long the x-axis
labels = ['{}'.format(v) for v in ticks]

# Plot the histogram
plt.hist(data=df, x='bike_id', bins=bins)

# The argument in the xscale() represents the axis scale type to apply.
plt.xscale('log')

# Apply x-ticks
plt.xticks(ticks, labels)
plt.xticks(rotation=90)

# labels and titles
plt.xlabel('Bike Id Range ')
plt.ylabel('Counts of Rides')
plt.title("Transformed Bike Id Distribution to log-type", y=1.05, fontsize=14, fontweight='bold');


# check number of rides' bike share 
share_counts = df.bike_share_for_all_trip.value_counts()
share_counts

No     166053
Yes     17359
Name: bike_share_for_all_trip, dtype: int64


# plotting bar and pie charts for bike share Proportion
# figure size and color palette
plt.figure(figsize =[15, 6])
base_color = sb.color_palette()[0]

# PIE CHART ON LEFT: Bike Share
plt.subplot(1,2, 1)
share_sorted_counts = df['bike_share_for_all_trip'].value_counts()
plt.pie(share_sorted_counts, labels = share_sorted_counts.index, autopct='%1.0f%%', startangle=90, counterclock = False)
plt.title("Bike Share Proportion", fontsize=15, y=1.05, fontweight='bold')
plt.axis('square');

# BAR CHART ON LEFT: Bike Share
plt.subplot(1,2, 2)
# Returns the sum of values in `bike_share_for_all_trip` column
n_share= df['bike_share_for_all_trip'].value_counts().sum()
# Recalculating the share_counts just to have clarity.
share_counts = df['bike_share_for_all_trip'].value_counts()
# Get the unique values of the `bike_share_for_all_trip` column, in the decreasing order of the frequency.
share_order = share_counts.index

# plot bar chart 
sb.countplot(data=df, x='bike_share_for_all_trip', order=share_order, palette="deep")
# get the current tick locations and labels
locs, labels = plt.xticks(rotation=90) 
plt.xlabel('Bike Share Satus')
plt.ylabel('Number of Bike Share')
plt.xticks(rotation=0)
plt.title("Bike Share Proportion", y=1.05, fontsize=14, fontweight='bold');

# loop through each pair of locations and labels
for loc, label in zip(locs, labels):

    # get the text property for the label to get the correct count
    count = share_counts[label.get_text()]
    pct_string = '{:1.0f}%'.format(100*count/n_share)

    # print the annotation just below the top of the bar
    plt.text(loc, count+2, pct_string, ha = 'center', color = 'black')


# check number of users per user type
user_counts = df.user_type.value_counts()
user_counts

Subscriber    163544
Customer       19868
Name: user_type, dtype: int64


# plotting bar and pie charts for user types
# figure size and color palette
plt.figure(figsize =[15, 6])
base_color = sb.color_palette()[0]

# PIE CHART ON LEFT: User Type
plt.subplot(1,2, 1)
user_sorted_counts = df['user_type'].value_counts()
plt.pie(user_sorted_counts, labels = user_sorted_counts.index, autopct='%1.0f%%', startangle=90, counterclock = False)
plt.title("User Type Proportion", fontsize=15, y=1.05, fontweight='bold')
plt.axis('square');

# BAR CHART ON LEFT: User Type
plt.subplot(1,2, 2)
# Returns the sum of all not-null values in `user_type` column
n_user= df['user_type'].value_counts().sum()
# Recalculating the day_counts just to have clarity.
user_counts = df['user_type'].value_counts()
# Get the unique values of the `user_type` column, in the decreasing order of the frequency.
user_order = user_counts.index

# plot bar chart 
sb.countplot(data=df, x='user_type', order=user_order, palette="deep")
# get the current tick locations and labels
locs, labels = plt.xticks(rotation=90) 
plt.xlabel('User Type')
plt.ylabel('Number of Users')
plt.xticks(rotation=0)
plt.title("User Type Proportion", y=1.05, fontsize=14, fontweight='bold');

# loop through each pair of locations and labels
for loc, label in zip(locs, labels):

    # get the text property for the label to get the correct count
    count = user_counts[label.get_text()]
    pct_string = '{:1.0f}%'.format(100*count/n_user)

    # print the annotation just below the top of the bar
    plt.text(loc, count+2, pct_string, ha = 'center', color = 'black')


# check number of members by females, males, and other
gender_counts = df.member_gender.value_counts()
gender_counts

Male           130651
Female          40844
Not_Defined      8265
Other            3652
Name: member_gender, dtype: int64


# plotting bar and pie charts for member genders
# figure size and color palette
plt.figure(figsize =[15, 6])
base_color = sb.color_palette()[0]

# PIE CHART ON LEFT: Trip Start Day
plt.subplot(1,2, 1)
member_sorted_counts = df['member_gender'].value_counts()
plt.pie(member_sorted_counts, labels = member_sorted_counts.index, autopct='%1.0f%%', startangle=90, counterclock = False)
plt.title("Member Gender Proportion", fontsize=15, y=1.05, fontweight='bold')
plt.axis('square');

# BAR CHART ON LEFT: Trip Start Day
plt.subplot(1,2, 2)
# Returns the sum of all not-null values in `member_gender` column
n_member= df['member_gender'].value_counts().sum()
# Recalculating the day_counts just to have clarity.
member_counts = df['member_gender'].value_counts()
# Get the unique values of the `member_gender` column, in the decreasing order of the frequency.
member_order = member_counts.index

# plot bar chart 
sb.countplot(data=df, x='member_gender', order=member_order, palette="deep")
# get the current tick locations and labels
locs, labels = plt.xticks(rotation=90) 
plt.xlabel('Member Gender')
plt.ylabel('Number of Members')
plt.xticks(rotation=0)
plt.title("Member Gender Proportion", y=1.05, fontsize=14, fontweight='bold');

# loop through each pair of locations and labels
for loc, label in zip(locs, labels):

    # get the text property for the label to get the correct count
    count = member_counts[label.get_text()]
    pct_string = '{:1.0f}%'.format(100*count/n_member)

    # print the annotation just below the top of the bar
    plt.text(loc, count+2, pct_string, ha = 'center', color = 'black')


# check 99%  statistic of member age
df['member_age'].describe(percentiles=[.99])

count    183412.000000
mean         32.652716
std          12.167516
min           0.000000
50%          31.000000
99%          63.000000
max         141.000000
Name: member_age, dtype: float64


# plotting histogram for member ages distribution
# figure size and color palette
base_color = sb.color_palette()[0]
plt.figure(figsize =[15, 6])

# HISTOGRAM ON LEFT: full data without scaling
plt.subplot(1,2, 1)
plt.hist(data = df, x = 'member_age')
plt.xlabel('Member Ages without Axis Scaling')
plt.ylabel('Counts of Rides')
plt.title("Non-scaled X-axis Member Ages", y=1.05, fontsize=14, fontweight='bold');


# HISTOGRAM ON RIGHT: data with x axis limits 
plt.subplot(1,2, 2)
plt.hist(data = df, x = 'member_age')
# x-axis scale limit
plt.xlim(0,70)
plt.xlabel('Member Ages with Axis Scaling')
plt.ylabel('Counts of Rides')
plt.title("Scaled X-axis Member Ages up to 100 years old", y=1.05, fontsize=14, fontweight='bold');


# check member age summary statistics and summary statistics to a scale of log10
(df.member_age.describe(), np.log10(df['member_age'].describe()))

C:\Users\sherif\anaconda3\lib\site-packages\pandas\core\series.py:726: RuntimeWarning: divide by zero encountered in log10
  result = getattr(ufunc, method)(*inputs, **kwargs)

(count    183412.000000
 mean         32.652716
 std          12.167516
 min           0.000000
 25%          26.000000
 50%          31.000000
 75%          38.000000
 max         141.000000
 Name: member_age, dtype: float64,
 count    5.263428
 mean     1.513919
 std      1.085202
 min          -inf
 25%      1.414973
 50%      1.491362
 75%      1.579784
 max      2.149219
 Name: member_age, dtype: float64)


# Axis transformation: x axis scaled and transformed to log-type for member age
# Bin size
bins = 10 ** np.arange(1, 2.5+0.05, 0.05)
# Generate the x-ticks you want to apply
ticks = [18, 20, 22,24, 26, 28,30, 32, 35, 37, 40, 42, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 110, 141]
# Convert ticks into string values, to be displaye dlong the x-axis
labels = ['{}'.format(v) for v in ticks]
# Plot the histogram
plt.figure(figsize =[15, 6])
plt.hist(data=df, x='member_age', bins=bins)
# The argument in the xscale() represents the axis scale type to apply.
plt.xscale('log')
# Apply x-ticks
plt.xticks(ticks, labels)
# labels and titles
plt.xlabel('Age/Year')
plt.ylabel('Counts of members')
plt.title("Transformed Member Age Distribution to Log-type ", y=1.05, fontsize=14, fontweight='bold');


# check the number of member ages <= 63 years old who constitutes 99% of member ages
most_ages = df[df['member_age'] <= 63]
print('Number of 99% of ages <= 63 years old: {}'.format(most_ages.member_age.value_counts().sum()))

# check the number of member ages > 63 years old who constitutes 1% of member ages
older_ages = df[df['member_age'] > 63]
print('Number of 1% of ages > 63 years old: {}'.format(older_ages.member_age.value_counts().sum()))

Number of 99% of ages <= 63 years old: 181598
Number of 1% of ages > 63 years old: 1814


# check the difference between members who have missing values in their records and members who are below 18 years old is 0
# check ages with 0 years old which filled and replaced NaN Values in'member_age'
age_0 = df.query("member_age == 0").value_counts().sum()
print('Number of ages = 0 years old: {}'.format(age_0))

# check ages under 18 years old
age_below_18 = df.query("member_age < 18").value_counts().sum()
print('Number of ages < 18 years old: {}'.format(age_below_18))

# calculate difference between age_0 and age_below_18
diff = df.query("member_age < 18").value_counts().sum() - df.query("member_age == 0").value_counts().sum()
print('Difference between ages = 0 years old and ages < 18 years old: {}'.format(diff))

Number of ages = 0 years old: 8265
Number of ages < 18 years old: 8265
Difference between ages = 0 years old and ages < 18 years old: 0


# Crear a new subset 'net_ages' which filters ages >= 18 and <= 63 years old
net_ages = df.query('member_age >=18 & member_age <= 63')
net_ages.shape[0]

173333


# plotting histogram for member ages distribution who are >= 18 years and <= 63 years 
""" 
Note: I have used the new subset 'net_ages' which filters ages >= 18 and <= 63 years old

"""
# figure size and color palette
base_color = sb.color_palette()[0]
plt.figure(figsize =[15, 6])

# HISTOGRAM ON LEFT: Non-scaled X-axis 18-63 Years-old Member Ages
plt.subplot(1,2, 1)
plt.hist(data = net_ages, x = 'member_age')
plt.xlabel('Member Age without Axis Scaling')
plt.ylabel('Counts of Rides')
plt.title("Non-scaled X-axis 18-63 Years-old Member Ages", y=1.05, fontsize=14, fontweight='bold');


# HISTOGRAM ON RIGHT: Scaled X-axis 18-63 Years-old Member Ages
plt.subplot(1,2, 2)
plt.hist(data = net_ages, x = 'member_age')
# x-axis scale limit
plt.xlim(15,65)
plt.xlabel('Member Age with Axis Scaling')
plt.ylabel('Counts of Rides')
plt.title("Scaled X-axis 18-63 Years-old Member Ages", y=1.05, fontsize=14, fontweight='bold');


# Axis transformation: x axis scaled and transformed to log-type
# Bin size
bins = 10 ** np.arange(1, 2.5+0.03, 0.03)
# Generate the x-ticks you want to apply
ticks = [18, 20, 23, 25, 27, 30, 32, 35, 37, 40, 42, 45, 50, 55, 60, 65]
# Convert ticks into string values, to be displaye dlong the x-axis
labels = ['{}'.format(v) for v in ticks]
# Plot the histogram
plt.figure(figsize =[15, 6])
plt.hist(data=net_ages, x='member_age', bins=bins)
# The argument in the xscale() represents the axis scale type to apply.
plt.xscale('log')
# Apply x-ticks
plt.xticks(ticks, labels)
# labels and titles
plt.xlabel('Member Age')
plt.ylabel('Counts of members')
plt.title("Transformed Log-type Distribution of 18-63 Years-old Member Ages", y=1.05, fontsize=14, fontweight='bold');


# create new subset of ages, exlcuding 0 age
ages_minus_0  = df[df['member_age'] > 0]
ages_minus_0.shape[0]

175147


# create member age groupings
df['net_age_groups'] = ages_minus_0['member_age'].apply(lambda x: '0-20' if 0<x<=20
                                                          else '20-30' if 20<x<=30
                                                          else '30-40' if 30<x<=40
                                                          else '40-50' if 40<x<=50
                                                          else '50-60' if 50<x<=60
                                                          else '60-70' if 60<x<=70
                                                          else '70-140' if 70<x<=150
                                                          else x)
df['net_age_groups'] = df['net_age_groups'].astype('category')


# plotting bar and pie charts for member age groupings
# figure size and color palette
plt.figure(figsize =[20, 6])
base_color = sb.color_palette()[0]

# PIE CHART ON LEFT: Trip Start Day
plt.subplot(1,2, 1)
sorted_groups = df['net_age_groups'].value_counts()
plt.pie(sorted_groups, labels = sorted_groups.index, autopct='%1.1f%%', startangle=60, 
        counterclock = False, rotatelabels = 30, explode =[0,0,0,1,1,1,1])
plt.title("Member Age Group Proportion", fontsize=15, y=1.05, fontweight='bold')
plt.axis('square')
plt.legend(title="Age", loc="center right");

# BAR CHART ON LEFT: Trip Start Day
plt.subplot(1,2, 2)
# Returns the sum of all not-null values in `member_gender` column
n_member= df['net_age_groups'].value_counts().sum()
# Recalculating the day_counts just to have clarity.
member_counts = df['net_age_groups'].value_counts()
# Get the unique values of the `member_gender` column, in the decreasing order of the frequency.
member_order = member_counts.index

# plot bar chart 
sb.countplot(data=df, x=df['net_age_groups'], order=member_order, palette="deep")
# get the current tick locations and labels
locs, labels = plt.xticks(rotation=90) 
plt.xlabel('Member Age Group')
plt.ylabel('Counts of Rides')
plt.xticks(rotation=0)
plt.title("Member Age Group/Number of Rides", y=1.05, fontsize=14, fontweight='bold');

# loop through each pair of locations and labels
for loc, label in zip(locs, labels):

    # get the text property for the label to get the correct count
    count = member_counts[label.get_text()]
    counter = '{0:0.0f}'.format(count)

    # print the annotation just below the top of the bar
    plt.text(loc, count+2, counter, ha = 'center', color = 'black')


# plotting bar chart for user types based on member gender
# figure size and color palette
plt.figure(figsize =[20, 6])

# BAR CHART ON LEFT: user types/member gender
plt.subplot(1,2,1)
# get 'user_type' as ordered types
user_order = df['user_type'].value_counts().index
# plot bar chart
sb.countplot(data = df, x = 'user_type', hue = 'member_gender', order = user_order)
plt.xlabel('User Type')
plt.ylabel('Counts per Member Gender')
plt.xticks(rotation=0)
plt.title("User Types Distribution/Member Gender", y=1.05, fontsize=14, fontweight='bold')
plt.legend(title="Gender", loc="upper right");

# BAR CHART ON MIDDLE: user types/member gender
plt.subplot(1,2,2)
# get 'user_type' as ordered types
user_order = df['user_type'].value_counts().index
# plot bar chart
sb.countplot(data = df, x = 'user_type', hue = 'bike_share_for_all_trip', order = user_order)
plt.xlabel('User Type')
plt.ylabel('Counts per Bike Share')
plt.xticks(rotation=0)
plt.title("User Types Distribution/Bike Share", y=1.05, fontsize=14, fontweight='bold')
plt.legend(title="Share Status", loc="upper right");


# check proportion of rides per user type and member gender
df.groupby('user_type')['member_gender'].value_counts(normalize=True)

user_type   member_gender
Customer    Male             0.580280
            Female           0.233894
            Not_Defined      0.162925
            Other            0.022901
Subscriber  Male             0.728379
            Female           0.221329
            Not_Defined      0.030744
            Other            0.019548
Name: member_gender, dtype: float64


# check proportion of rides per user type and bike share status
df.groupby('user_type')['bike_share_for_all_trip'].value_counts(normalize=True)

user_type   bike_share_for_all_trip
Customer    No                         1.000000
Subscriber  No                         0.893857
            Yes                        0.106143
Name: bike_share_for_all_trip, dtype: float64


# plotting bar chart for user types based on member gender

# figure size and color palette
plt.figure(figsize =[20, 6])

# get 'user_type' as ordered types
usr_order = df['user_type'].value_counts().index
# plot bar chart
sb.countplot(data = df, x = 'user_type', hue = 'net_age_groups', order = usr_order)
plt.xlabel('Age Group')
plt.ylabel('Counts of rides per age group')
plt.xticks(rotation=0)
plt.title("User Types Distribution/Age Group", y=1.05, fontsize=14, fontweight='bold')
plt.legend(title="Age Group", loc="upper right");


# check proportion of rides per User Type vs. Age Groups
df.groupby('user_type')['net_age_groups'].value_counts(normalize=True)

user_type   net_age_groups
Customer    20-30             0.397270
            30-40             0.383861
            40-50             0.114064
            50-60             0.059828
            0-20              0.033311
            60-70             0.010583
            70-140            0.001082
Subscriber  20-30             0.400641
            30-40             0.362752
            40-50             0.127135
            50-60             0.065173
            0-20              0.023253
            60-70             0.017531
            70-140            0.003514
Name: net_age_groups, dtype: float64


# plotting bar chart for user types based on start and end stations
# figure size
plt.figure(figsize =[20, 20])

# HEAAT MAP ON LEFT: user types/start station
plt.subplot(1,2,1)
# Use group_by() and size() to get the number of start stations and each combination
# of the two variable levels as a pandas Series
start_counts = df.groupby(['user_type', 'start_station_name']).size()

# Use Series.reset_index() to convert a series into a dataframe object
start_counts = start_counts.reset_index(name='count')

# Use DataFrame.pivot() to rearrange the data, to have user types on rows
start_counts = start_counts.pivot(index = 'start_station_name', columns = 'user_type', values = 'count')

# plot heat map
sb.heatmap(start_counts, cmap ='rocket_r')
plt.xlabel('User Type')
plt.ylabel('Start Stations')
plt.yticks(rotation=30)
plt.title("User Types Distribution/Start Stations", y=1.05, fontsize=14, fontweight='bold');

# HEAAT MAP ON RIGHT: user types/end station
plt.subplot(1,2,2)
# Use group_by() and size() to get the number of start stations and each combination
# of the two variable levels as a pandas Series
end_counts = df.groupby(['user_type', 'end_station_name']).size()

# Use Series.reset_index() to convert a series into a dataframe object
end_counts = end_counts.reset_index(name='count')

# Use DataFrame.pivot() to rearrange the data, to have user types on rows
end_counts = end_counts.pivot(index = 'end_station_name', columns = 'user_type', values = 'count')

# plot heat map
sb.heatmap(end_counts, cmap ='rocket_r')
plt.xlabel('User Type')
plt.ylabel('End Stations')
plt.yticks(rotation=30)
plt.title("User Types Distribution/End Stations", y=1.05, fontsize=14, fontweight='bold');


# plotting bar chart for user types based on member gender
# figure size and color palette
plt.figure(figsize =[20, 6])

# get 'user_type' as ordered types
share_order = df['member_gender'].value_counts().index
# plot bar chart
sb.countplot(data = df, x = 'member_gender', hue = 'bike_share_for_all_trip', order = share_order)
plt.xlabel('Member Genders')
plt.ylabel('Counts per Bike Share Status')
plt.xticks(rotation=0)
plt.title("Member Genders Distribution/Bike Share Status", y=1.05, fontsize=14, fontweight='bold')
plt.legend(title="Bike Share Status", loc="upper right");


# check proportion of rides per member gender and bike share status
df.groupby('member_gender')['bike_share_for_all_trip'].value_counts(normalize=True)

member_gender  bike_share_for_all_trip
Female         No                         0.911860
               Yes                        0.088140
Male           No                         0.899649
               Yes                        0.100351
Not_Defined    No                         1.000000
Other          No                         0.822563
               Yes                        0.177437
Name: bike_share_for_all_trip, dtype: float64


# plotting bar chart for Member Gender vs. Age Groups 

# figure size and color palette
plt.figure(figsize =[20, 6])

# get 'user_type' as ordered types
member_order = df['member_gender'].value_counts().index
# plot bar chart
sb.countplot(data = df, x = 'member_gender', hue = 'net_age_groups', order = member_order)
plt.xlabel('Member Gender')
plt.ylabel('Counts of rides per Member Gender')
plt.xticks(rotation=0)
plt.title("Member Gender Distribution/Age Group", y=1.05, fontsize=14, fontweight='bold')
plt.legend(title="Age Group", loc="upper right");


# check proportion of rides per member gender and bike share status
df.groupby('member_gender')['net_age_groups'].value_counts(normalize=True)

member_gender  net_age_groups
Female         20-30             0.440407
               30-40             0.365096
               40-50             0.105009
               50-60             0.051954
               0-20              0.025291
               60-70             0.010479
               70-140            0.001763
Male           20-30             0.389871
               30-40             0.363013
               40-50             0.132613
               50-60             0.067217
               0-20              0.024477
               60-70             0.019173
               70-140            0.003636
Other          30-40             0.423330
               20-30             0.325849
               40-50             0.119113
               50-60             0.115553
               70-140            0.007667
               60-70             0.006024
               0-20              0.002464
Name: net_age_groups, dtype: float64


# plotting heatmap for member genders based on start and end stations
# figure size
plt.figure(figsize =[20, 20])

# HEAAT MAP ON LEFT: member gender/start station
plt.subplot(1,2,1)
# Use group_by() and size() to get the number of start stations and each combination 
# of the two variable levels as a pandas Series
start_counts = df.groupby(['member_gender', 'start_station_name']).size()

# Use Series.reset_index() to convert a series into a dataframe object
start_counts = start_counts.reset_index(name='count')

# Use DataFrame.pivot() to rearrange the data, to have member gender on rows
start_counts = start_counts.pivot(index = 'start_station_name', columns = 'member_gender', values = 'count')

# plot heat map
sb.heatmap(start_counts, cmap ='rocket_r')
plt.xlabel('Member Genders')
plt.ylabel('Start Stations')
plt.yticks(rotation=30)
plt.title("Member Genders Distribution/Start Stations", y=1.05, fontsize=14, fontweight='bold');

# HEAAT MAP ON RIGHT: member gender/end station
plt.subplot(1,2,2)
# Use group_by() and size() to get the number of start stations and each combination
# of the two variable levels as a pandas Series
end_counts = df.groupby(['member_gender', 'end_station_name']).size()

# Use Series.reset_index() to convert a series into a dataframe object
end_counts = end_counts.reset_index(name='count')

# Use DataFrame.pivot() to rearrange the data, to have member gender on rows
end_counts = end_counts.pivot(index = 'end_station_name', columns = 'member_gender', values = 'count')

# plot heat map
sb.heatmap(end_counts, cmap ='rocket_r')
plt.xlabel('Member Genders')
plt.ylabel('End Stations')
plt.yticks(rotation=30)
plt.title("Member Genders Distribution/End Stations", y=1.05, fontsize=14, fontweight='bold');


# plotting bar chart for uBike Share vs. Age Groups

# figure size and color palette
plt.figure(figsize =[20, 6])

# get 'user_type' as ordered types
grop_order = df['bike_share_for_all_trip'].value_counts().index
# plot bar chart
sb.countplot(data = df, x = 'bike_share_for_all_trip', hue = 'net_age_groups', order = grop_order)
plt.xlabel('Bike Share Status')
plt.ylabel('Counts of rides per age group')
plt.xticks(rotation=0)
plt.title("Bike Share Status Distribution/Age Group", y=1.05, fontsize=14, fontweight='bold')
plt.legend(title="Age Group", loc="upper right");


# check proportion of rides per member gender and bike share status
df.groupby('bike_share_for_all_trip')['net_age_groups'].value_counts(normalize=True)

bike_share_for_all_trip  net_age_groups
No                       30-40             0.390442
                         20-30             0.379585
                         40-50             0.125928
                         50-60             0.063674
                         0-20              0.022093
                         60-70             0.015806
                         70-140            0.002472
Yes                      20-30             0.588801
                         30-40             0.131286
                         40-50             0.125583
                         50-60             0.073679
                         0-20              0.043436
                         60-70             0.026557
                         70-140            0.010657
Name: net_age_groups, dtype: float64


# plotting heatmap for Bike Share vs. Ride Stations
# figure size
plt.figure(figsize =[20, 20])

# HEAAT MAP ON LEFT:Boke Share Status/start station
plt.subplot(1,2,1)
# Use group_by() and size() to get the number of start stations and each combination 
# of the two variable levels as a pandas Series
start_counts = df.groupby(['bike_share_for_all_trip', 'start_station_name']).size()

# Use Series.reset_index() to convert a series into a dataframe object
start_counts = start_counts.reset_index(name='count')

# Use DataFrame.pivot() to rearrange the data, to have member gender on rows
start_counts = start_counts.pivot(index = 'start_station_name', columns = 'bike_share_for_all_trip', values = 'count')

# plot heat map
sb.heatmap(start_counts, cmap ='rocket_r')
plt.xlabel('Bike Share Status')
plt.ylabel('Start Stations')
plt.yticks(rotation=30)
plt.title("Bike Share StatusDistribution/Start Stations", y=1.05, fontsize=14, fontweight='bold');

# HEAAT MAP ON RIGHT: Boke Share Status/end station
plt.subplot(1,2,2)
# Use group_by() and size() to get the number of start stations and each combination
# of the two variable levels as a pandas Series
end_counts = df.groupby(['bike_share_for_all_trip', 'end_station_name']).size()

# Use Series.reset_index() to convert a series into a dataframe object
end_counts = end_counts.reset_index(name='count')

# Use DataFrame.pivot() to rearrange the data, to have member gender on rows
end_counts = end_counts.pivot(index = 'end_station_name', columns = 'bike_share_for_all_trip', values = 'count')

# plot heat map
sb.heatmap(end_counts, cmap ='rocket_r')
plt.xlabel('Bike Share Status')
plt.ylabel('End Stations')
plt.yticks(rotation=30)
plt.title("Bike Share Status Distribution/End Stations", y=1.05, fontsize=14, fontweight='bold');


# plotting scatter plot for Member Age vs. Ride Duration in Seconds, Minutes, Hours

""" 
Note: I have used ages_minus_0 subset to plot ages, excluding 0 ages
      to better visualize data distribution
"""

# figure size
plt.figure(figsize =[20, 6])

# SCATTER PLOT ON LEFT: Member Age vs. Ride Duration in Seconds
plt.subplot(1,3,1)
# scatter plot 
sb.regplot(x=ages_minus_0['member_age'], y=ages_minus_0['duration_sec'], fit_reg=False)
plt.xlabel('Member Ages (year)')
plt.ylabel('Duration (second)')
plt.xticks(rotation=0)
plt.title("Member Ages/Duration(second)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON MIDDLE: Member Age vs. Ride Duration in Minutes
plt.subplot(1,3,2)
# scatter plot 
sb.regplot(x=ages_minus_0['member_age'], y=ages_minus_0['duration_minute'],fit_reg=False)
plt.xlabel('Member Ages (year)')
plt.ylabel('Duration (minute)')
plt.xticks(rotation=0)
plt.title("Member Ages/Duration (minute)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON RIGHT: Member Age vs. Ride Duration in Hours
plt.subplot(1,3,3)
# scatter plot 
sb.regplot(x=ages_minus_0['member_age'], y=ages_minus_0['duration_hour'], fit_reg=False)
plt.xlabel('Member Ages (year)')
plt.ylabel('Duration (hour)')
plt.xticks(rotation=0)
plt.title("Member Ages/Duration (hour)", y=1.05, fontsize=14, fontweight='bold');


# plotting heat map for Member Age vs. Ride Start Time Hour and End Time Hour 

""" 
Note: I have used ages_minus_0 subset to plot ages, excluding 0 ages
      to better visualize data distribution
"""

# figure size
plt.figure(figsize =[20, 6])

# HEAT MAP ON LEFT:  Member Age vs. Ride Start Time Hour
plt.subplot(1,2,1)
# plot heat map 
plt.hist2d(data = ages_minus_0, x = ages_minus_0['member_age'], y = ages_minus_0['start_time_hour'], cmap = 'viridis_r')
plt.xlabel('Member Ages (year)')
plt.ylabel('Start Time(hour)')
plt.xticks(rotation=0)
plt.title("Member Ages/Start Time(hour)", y=1.05, fontsize=14, fontweight='bold')
plt.colorbar(label = 'Number of Rides');

# HEAT MAP ON RIGHT:  Member Age vs. Ride End Time Hour
plt.subplot(1,2,2)
# plot heat map 
plt.hist2d(data = ages_minus_0, x = ages_minus_0['member_age'], y = ages_minus_0['end_time_hour'], cmap = 'viridis_r')
plt.xlabel('Member Ages (year)')
plt.ylabel('End Time(hour)')
plt.xticks(rotation=0)
plt.title("Member Ages/End Time(hour)", y=1.05, fontsize=14, fontweight='bold')
plt.colorbar(label = 'Number of Rides');


# plotting heat map for Member Age vs. Ride Start Time Day and End Time Day 


""" 
Note: I have used ages_minus_0 subset to plot ages, excluding 0 ages
      to better visualize data distribution
"""

# figure size
plt.figure(figsize =[20, 6])

# HEAT MAP ON LEFT:  Member Age vs. Ride Start Time Day
plt.subplot(1,2,1)
# plot heat map 
plt.hist2d(data = ages_minus_0, x = ages_minus_0['member_age'], y = ages_minus_0['start_time_day'], cmap = 'viridis_r')
plt.xlabel('Member Ages (year)')
plt.ylabel('Start Time(day)')
plt.xticks(rotation=0)
plt.title("Member Ages/Start Time(day)", y=1.05, fontsize=14, fontweight='bold')
plt.colorbar(label = 'Number of Rides');

# HEAT MAP ON RIGHT:  Member Age vs. Ride End Time Day
plt.subplot(1,2,2)
# plot heat map 
plt.hist2d(data = ages_minus_0, x = ages_minus_0['member_age'], y = ages_minus_0['end_time_day'], cmap = 'viridis_r')
plt.xlabel('Member Ages (year)')
plt.ylabel('End Time(day)')
plt.xticks(rotation=0)
plt.title("Member Ages/End Time(day)", y=1.05, fontsize=14, fontweight='bold')
plt.colorbar(label = 'Number of Rides');


# check the summary statistics as log type to scale 'distance_km' 
np.log10(ages_minus_0['distance_km']).describe()

C:\Users\sherif\anaconda3\lib\site-packages\pandas\core\series.py:726: RuntimeWarning: divide by zero encountered in log10
  result = getattr(ufunc, method)(*inputs, **kwargs)

count    1.751470e+05
mean             -inf
std               NaN
min              -inf
25%     -4.205788e-02
50%      1.549003e-01
75%      3.467890e-01
max      1.841793e+00
Name: distance_km, dtype: float64


# plotting heat map for Member Age vs. Distance(km)

""" 
Note: I have used ages_minus_0 subset to plot ages, excluding 0 ages
      to better visualize data distribution
"""

# figure size
plt.figure(figsize =[15, 6])

# transform distance(km) to scale the y axis to the most travelled distancs
def log_trans(x, inverse = False):
    if not inverse:
        return np.log10(x)
    else:
        return np.power(10, x)
    
# plot scatter plot
sb.regplot(x=ages_minus_0['member_age'], y=ages_minus_0['distance_km'].apply(log_trans), truncate=False, x_jitter=.3,
           fit_reg=True, scatter_kws={'alpha':1/20})
# y-axis tik scales
tick_locs = [0.1, 0.2, 0.3, 0.5, 1, 2, 5, 8, 20, 50, 70]
plt.yticks(log_trans(tick_locs), tick_locs)
# labels and title
plt.xlabel('Member Ages (year)')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Member Ages/Distance(km) - Scatter Plot", y=1.05, fontsize=14, fontweight='bold');


# find distance without outlier
ages_minus_0['distance_km'].describe(percentiles = [.999])

count    175147.000000
mean          1.689329
std           1.096938
min           0.000000
50%           1.428566
99.9%         7.087275
max          69.469241
Name: distance_km, dtype: float64


# plotting scatter plot and heat map for Member Age vs. Bike Id

""" 
Note: I have used ages_minus_0 subset to plot ages, excluding 0 ages
      to better visualize data distribution
"""

# transform distance(km) to scale the y axis to the most travelled distancs
def log_trans(x, inverse = False):
    if not inverse:
        return np.log10(x)
    else:
        return np.power(10, x)
    
# figure size
plt.figure(figsize =[15, 6])

# SCATTER PLOT ON LEFT:  Member Age vs. Bike Id
plt.subplot(1,2,1)
# plot scatter plot
sb.regplot(x=ages_minus_0['member_age'], y=ages_minus_0['bike_id'].apply(log_trans), truncate=False, x_jitter=.8, fit_reg=True,
           scatter_kws={'alpha':.8})
# y-axis tik scales
tick_locs = [10, 30, 50, 100, 200, 300, 500, 1000, 2000, 3000, 5000, 7000]
plt.yticks(log_trans(tick_locs), tick_locs)
# labels and title
plt.xlabel('Member Ages (year)')
plt.ylabel('Range of Bike Ids')
plt.xticks(rotation=0)
plt.title("Member Ages Distribution/Bike Ids", y=1.05, fontsize=14, fontweight='bold');


# HEAT MAP ON RIGHT: Member Age vs. Bike Id
plt.subplot(1,2,2)
# plot heat map 
plt.hist2d(data = ages_minus_0, x = ages_minus_0['member_age'], y = ages_minus_0['bike_id'], cmap = 'viridis_r')
# labels and title
plt.xlabel('Member Ages (year)')
plt.ylabel('Range of Bike Ids')
plt.xticks(rotation=0)
plt.title("Member Ages Distribution/Bike Ids", y=1.05, fontsize=14, fontweight='bold')
plt.colorbar(label = 'Counts of Rides');


# plotting scatter plot for  Ride Start and End Time Hour vs. Ride Duration in Seconds, Minutes, and Hours (6 Plots)

# figure size
plt.figure(figsize =[20, 20])

# SCATTER PLOT ON ROW 1, LEFT: Ride Start Time Hour vs. Ride Duration in Seconds
plt.subplot(3,2,1)
# scatter plot 
sb.regplot(x=df['start_time_hour'], y=df['duration_sec'], fit_reg=False)
plt.xlabel('Start Time Hour')
plt.ylabel('Duration (second)')
plt.xticks(rotation=0)
plt.title("Start Time Hour/Duration(second)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON ROW 1, RIGHT: Ride End Time Hour vs. Ride Duration in Seconds
plt.subplot(3,2,2)
# scatter plot 
sb.regplot(x=df['end_time_hour'], y=df['duration_sec'], fit_reg=False)
plt.xlabel('End Time Hour')
plt.ylabel('Duration (second)')
plt.xticks(rotation=0)
plt.title("End Time Hour/Duration(second)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON ROW 2, LEFT: Ride Start Time Hour vs. Ride Duration in Minutes
plt.subplot(3,2,3)
# scatter plot 
sb.regplot(x=df['start_time_hour'], y=df['duration_minute'],fit_reg=False)
plt.xlabel('Start Time Hour')
plt.ylabel('Duration (minute)')
plt.xticks(rotation=0)
plt.title("Start Time Hour/Duration(minute)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON ROW 2, RIGHT: Ride End Time Hour vs. Ride Duration in Minutes
plt.subplot(3,2,4)
# scatter plot 
sb.regplot(x=df['end_time_hour'], y=df['duration_minute'],fit_reg=False)
plt.xlabel('End Time Hour')
plt.ylabel('Duration (minute)')
plt.xticks(rotation=0)
plt.title("End Time Hour/Duration(minute)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON ROW 3, LEFT: Ride Start Time Hour vs. Ride Duration in Hours
plt.subplot(3,2,5)
# scatter plot 
sb.regplot(x=df['start_time_hour'], y=df['duration_hour'], fit_reg=False)
plt.xlabel('Start Time Hour')
plt.ylabel('Duration (hour)')
plt.xticks(rotation=0)
plt.title("Start Time Hour/Duration(hour)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON ROW 3, RIGHT: Ride End Time Hour vs. Ride Duration in Hours
plt.subplot(3,2,6)
# scatter plot 
sb.regplot(x=df['end_time_hour'], y=df['duration_hour'], fit_reg=False)
plt.xlabel('End Time Hour')
plt.ylabel('Duration (hour)')
plt.xticks(rotation=0)
plt.title("End Time Hour/Duration(hour)", y=1.05, fontsize=14, fontweight='bold');


# plotting scatter plot for  Ride Start and End Time Day vs. Ride Duration in Seconds, Minutes, and Hours (6 Plots)


# figure size
plt.figure(figsize =[20, 20])

# SCATTER PLOT ON ROW 1, LEFT: Ride Start Time Day vs. Ride Duration in Seconds
plt.subplot(3,2,1)
# scatter plot 
sb.regplot(x=df['start_time_day'], y=df['duration_sec'], fit_reg=False)
plt.xlabel('Start Time Day')
plt.ylabel('Duration (hour)')
plt.xticks(rotation=0)
plt.title("Start Time Day/Duration(second)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON ROW 1, RIGHT: Ride End Time Day vs. Ride Duration in Seconds
plt.subplot(3,2,2)
# scatter plot 
sb.regplot(x=df['end_time_day'], y=df['duration_sec'], fit_reg=False)
plt.xlabel('End Time Day')
plt.ylabel('Duration (hour)')
plt.xticks(rotation=0)
plt.title("End Time Day/Duration(second)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ROW 2, LEFT:  Ride Start Time Day vs. Ride Duration in Minutes
plt.subplot(3,2,3)
# scatter plot 
sb.regplot(x=df['start_time_day'], y=df['duration_minute'], fit_reg=False)
plt.xlabel('Start Time Day')
plt.ylabel('Duration (hour)')
plt.xticks(rotation=0)
plt.title("Start Time Day/Duration(minute)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON ROW 2, RIGHT:  Ride End Time Day vs. Ride Duration in Minutes
plt.subplot(3,2,4)
# scatter plot 
sb.regplot(x=df['end_time_day'], y=df['duration_minute'], fit_reg=False)
plt.xlabel('End Time Day')
plt.ylabel('Duration (hour)')
plt.xticks(rotation=0)
plt.title("End Time Day/Duration(minute)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON ROW 3, LEFT: Ride Start Time Day vs. Ride Duration in Hours
plt.subplot(3,2,5)
# scatter plot 
sb.regplot(x=df['start_time_day'], y=df['duration_hour'], fit_reg=False)
plt.xlabel('Start Time Day')
plt.ylabel('Duration (hour)')
plt.xticks(rotation=0)
plt.title("Start Time Day/Duration(hour)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON ROW 3, RIGHT: Ride End Time Day vs. Ride Duration in Hours
plt.subplot(3,2,6)
# scatter plot 
sb.regplot(x=df['end_time_day'], y=df['duration_hour'], fit_reg=False)
plt.xlabel('End Time Day')
plt.ylabel('Duration (hour)')
plt.xticks(rotation=0)
plt.title("End Time Day/Duration(hour)", y=1.05, fontsize=14, fontweight='bold');


# plotting scatter plot for  Ride Start and End Time Hour vs. Ride Duration in Seconds, Minutes, and Hours (6 Plots)

# figure size
plt.figure(figsize =[20, 6])

# transform distance(km) to scale the y axis to the most travelled distancs
def log_trans(x, inverse = False):
    if not inverse:
        return np.log10(x)
    else:
        return np.power(10, x)
    
# SCATTER PLOT ON LEFT:  Ride Start Time Hour vs. Ride distance
plt.subplot(1,2,1)
# plot scatter plot
sb.regplot(df['start_time_hour'], df['distance_km'].apply(log_trans), truncate=False, x_jitter=.5, fit_reg=False,
           scatter_kws={'alpha':0.3})
# y-axis tik scales
tick_locs = [0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.5, 1, 2, 5, 10, 30, 50, 100]
plt.yticks(log_trans(tick_locs), tick_locs)
# labels and title
plt.xlabel('Start Time Hour')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Start Time Hour Distribution/Distance(km)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON RIGHT:  Ride End Time Hour vs. Ride distance
plt.subplot(1,2,2)
# plot scatter plot
sb.regplot(df['start_time_hour'], df['distance_km'].apply(log_trans), truncate=False, x_jitter=.5, fit_reg=False,
           scatter_kws={'alpha':0.3})
# y-axis tik scales
tick_locs = [0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.5, 1, 2, 5, 10, 30, 50, 100]
plt.yticks(log_trans(tick_locs), tick_locs)
# labels and title
plt.xlabel('End Time Hour')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("End Time Hour Distribution/Distance(km)", y=1.05, fontsize=14, fontweight='bold');

C:\Users\sherif\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(


# plotting scatter plot for  Ride Start and End Time Day vs. Ride Duration in Seconds, Minutes, and Hours (6 Plots)

# figure size
plt.figure(figsize =[20, 6])

# transform distance(km) to scale the y axis to the most travelled distancs
def log_trans(x, inverse = False):
    if not inverse:
        return np.log10(x)
    else:
        return np.power(10, x)
    
# SCATTER PLOT ON LEFT:  Ride Start Time Day vs. Ride distance
plt.subplot(1,2,1)
# plot scatter plot
sb.regplot(df['start_time_day'], df['distance_km'].apply(log_trans), truncate=False, x_jitter=.5, fit_reg=False,
           scatter_kws={'alpha':0.3})
# y-axis tik scales
tick_locs = [0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.5, 1, 2, 5, 10, 30, 50, 100]
plt.yticks(log_trans(tick_locs), tick_locs)
# labels and title
plt.xlabel('Start Time Day')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Start Time Day Distribution/Distance(km)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON RIGHT:  Ride End Time Day vs. Ride distance
plt.subplot(1,2,2)
# plot scatter plot
sb.regplot(df['end_time_day'], df['distance_km'].apply(log_trans), truncate=False, x_jitter=.5, fit_reg=False,
           scatter_kws={'alpha':0.3})
# y-axis tik scales
tick_locs = [0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.5, 1, 2, 5, 10, 30, 50, 100]
plt.yticks(log_trans(tick_locs), tick_locs)
# labels and title
plt.xlabel('End Time Day')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("End Time Day Distribution/Distance(km)", y=1.05, fontsize=14, fontweight='bold');


# plotting scatter plot for Ride Start and End Time Hour vs. Bike Id

# figure size
plt.figure(figsize =[20, 15])

# transform bike ids to scale the y axis to the most travelled distancs
def log_trans(x, inverse = False):
    if not inverse:
        return np.log10(x)
    else:
        return np.power(10, x)
    
# SCATTER PLOT ON ROW1, LEFT:  Ride Start Time hour vs. vs. Bike Ide
plt.subplot(2,2,1)
# plot scatter plot
sb.regplot(x=df['start_time_hour'], y=df['bike_id'].apply(log_trans), truncate=False, x_jitter=.5, fit_reg=False,
           scatter_kws={'alpha':0.3})
# y-axis tik scales
tick_locs = [10, 100, 1000, 3000, 5000, 7000]
plt.yticks(log_trans(tick_locs), tick_locs)
# labels and title
plt.xlabel('Start Time Hour')
plt.ylabel('Range of Bike Ids)')
plt.xticks(rotation=0)
plt.title("Start Time Hour Distribution/Bike Ids", y=1.05, fontsize=14, fontweight='bold');

# HEAT MAP ON ROW 1, RIGHT: Ride Start Time hour vs. vs. Bike Ide
plt.subplot(2,2,2)
# plot heat map 
plt.hist2d(data = df, x = df['start_time_hour'], y = df['bike_id'], cmap = 'viridis_r')
# labels and title
plt.xlabel('Start Time Hour')
plt.ylabel('Range of Bike Ids')
plt.xticks(rotation=0)
plt.title("Start Time Hour Distribution/Bike Ids", y=1.05, fontsize=14, fontweight='bold')
plt.colorbar(label = 'Number of Rides');


# SCATTER PLOT ON ROW 2, LEFT:  Ride End Time hour vs. vs. Bike Ide
plt.subplot(2,2,3)
# plot scatter plot
sb.regplot(df['end_time_hour'], df['bike_id'].apply(log_trans), truncate=False, x_jitter=.5, fit_reg=False,
           scatter_kws={'alpha':0.5})
# y-axis tik scales
tick_locs = [10, 100, 1000, 3000, 5000, 7000]
plt.yticks(log_trans(tick_locs), tick_locs)
# labels and title
plt.xlabel('End Time Hour')
plt.ylabel('Range of Bike Ids)')
plt.xticks(rotation=0)
plt.title("End Time Hour Distribution/Bike Ids", y=1.05, fontsize=14, fontweight='bold');

# HEAT MAP ON ROW 2, RIGHT: Ride End Time hour vs. vs. Bike Ide
plt.subplot(2,2,4)
# plot heat map 
plt.hist2d(data = df, x = df['end_time_hour'], y = df['bike_id'], cmap = 'viridis_r')
# labels and title
plt.xlabel('End Time Hour')
plt.ylabel('Range of Bike Ids')
plt.xticks(rotation=0)
plt.title("End Time Hour Distribution/Bike Ids", y=1.05, fontsize=14, fontweight='bold')
plt.colorbar(label = 'Number of Rides');


# plotting scatter plot for Ride Start and End Time Day vs. Bike Id

# figure size
plt.figure(figsize =[20, 15])

# transform bike ids to scale the y axis to the most travelled distancs
def log_trans(x, inverse = False):
    if not inverse:
        return np.log10(x)
    else:
        return np.power(10, x)
    
# SCATTER PLOT ON ROW 1, LEFT:  Ride Start Time Day vs. vs. Bike Ide
plt.subplot(2,2,1)
# plot scatter plot
sb.regplot(df['start_time_day'], df['bike_id'].apply(log_trans), truncate=False, x_jitter=.5, fit_reg=False,
           scatter_kws={'alpha':0.3})
# y-axis tik scales
tick_locs = [10, 100, 1000, 3000, 5000, 7000]
plt.yticks(log_trans(tick_locs), tick_locs)
# labels and title
plt.xlabel('Start Time Day')
plt.ylabel('Range of Bike Ids)')
plt.xticks(rotation=0)
plt.title("Start Time Day Distribution/Bike Ids", y=1.05, fontsize=14, fontweight='bold');

# HEAT MAP ON ROW 1, RIGHT: Ride Start Time Day vs. vs. Bike Ide
plt.subplot(2,2,2)
# plot heat map 
plt.hist2d(data = df, x = df['start_time_day'], y = df['bike_id'], cmap = 'viridis_r')
# labels and title
plt.xlabel('Start Time Day')
plt.ylabel('Range of Bike Ids')
plt.xticks(rotation=0)
plt.title("Start Time Day Distribution/Bike Ids", y=1.05, fontsize=14, fontweight='bold')
plt.colorbar(label = 'Number of Rides');


# SCATTER PLOT ON ROW 2, LEFT:  Ride End Time Day vs. vs. Bike Ide
plt.subplot(2,2,3)
# plot scatter plot
sb.regplot(df['end_time_day'], df['bike_id'].apply(log_trans), truncate=False, x_jitter=.5, fit_reg=False,
           scatter_kws={'alpha':0.3})
# y-axis tik scales
tick_locs = [10, 100, 1000, 3000, 5000, 7000]
plt.yticks(log_trans(tick_locs), tick_locs)
# labels and title
plt.xlabel('End Time Day')
plt.ylabel('Range of Bike Ids)')
plt.xticks(rotation=0)
plt.title("End Time Day Distribution/Bike Ids", y=1.05, fontsize=14, fontweight='bold');

# HEAT MAP ON ROW 2, RIGHT: Ride End Time Day vs. vs. Bike Ide
plt.subplot(2,2,4)
# plot heat map 
plt.hist2d(data = df, x = df['end_time_day'], y = df['bike_id'], cmap = 'viridis_r')
# labels and title
plt.xlabel('End Time Day')
plt.ylabel('Range of Bike Ids')
plt.xticks(rotation=0)
plt.title("End Time Day Distribution/Bike Ids", y=1.05, fontsize=14, fontweight='bold')
plt.colorbar(label = 'Number of Rides');


# plotting scatter plot for Ride Duration in Seconds, Minutes, and Hours vs. Bike Id (3 Plots)



# transform bike ids to scale the y axis to the most travelled distancs
def log_trans(y, inverse = False):
    if not inverse:
        return np.log10(y)
    else:
        return np.power(10, y)
    
# figure size
plt.figure(figsize =[20, 6])

# SCATTER PLOT ON LEFT: Duration in Seconds vs. Bike Ids
plt.subplot(1,3,1)
# scatter plot 
sb.regplot(df['duration_sec'].apply(log_trans), df['bike_id'], fit_reg=False)
# x-axis tik scales
tick_locs = [10, 100, 1000, 10000, 100000]
plt.xticks(log_trans(tick_locs), tick_locs)
# labels and title
plt.xlabel('Duration in Seconds')
plt.ylabel('Range of Bike Ids)')
plt.xticks(rotation=0)
plt.title("Duration in Seconds/Bike Ids", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON MIDDLE: Duration in Minutes vs. Bike Ids
plt.subplot(1,3,2)
# scatter plot 
sb.regplot(df['duration_minute'].apply(log_trans), df['bike_id'], fit_reg=False)
# x-axis tik scales
tick_locs = [1, 5, 10, 20, 50, 100, 200, 500, 1500]
plt.xticks(log_trans(tick_locs), tick_locs)
# labels and title
plt.xlabel('Duration in Minutes')
plt.ylabel('Range of Bike Ids)')
plt.xticks(rotation=0)
plt.title("Duration in Minutes/Bike Ids", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON ROW RIGHT: Duration in Hours vs. Bike Ids
plt.subplot(1,3,3)
# scatter plot 
sb.regplot(df['duration_hour'].apply(log_trans), df['bike_id'], fit_reg=False)
# x-axis tik scales
tick_locs = [0.1, 0.2, 0.3, 0.5, 1, 2, 3, 5, 10, 20, 30]
plt.xticks(log_trans(tick_locs), tick_locs)
# labels and title
plt.xlabel('Duration in Hours')
plt.ylabel('Range of Bike Ids)')
plt.xticks(rotation=0)
plt.title("Duration in Hours/Bike Ids", y=1.05, fontsize=14, fontweight='bold');


# plotting scatter plot for Ride Duration in Seconds, Minutes, and Hours vs. Ride Distance 

# transform distance(km) to scale the y axis to the most travelled distancs
def log_trans(x, inverse = False):
    if not inverse:
        return np.log10(x)
    else:
        return np.power(10, x)
    
# figure size
plt.figure(figsize =[20, 6])

# SCATTER PLOT ON LEFT: Duration in Seconds vs. Ride Distance 

plt.subplot(1,3,1)
# scatter plot 
sb.regplot(x=df['duration_sec'], y=df['distance_km'].apply(log_trans), fit_reg=False)
# x-axis tik scales
tick_locs = [0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.5, 1, 2, 5, 10, 20, 30, 50, 100]
plt.yticks(log_trans(tick_locs), tick_locs)
# labels and title
plt.xlabel('Duration in Seconds')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Duration in Seconds/Distance(km)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON MIDDLE: Duration in Minutes vs. Ride Distance 
plt.subplot(1,3,2)
# scatter plot 
sb.regplot(x=df['duration_minute'], y=df['distance_km'].apply(log_trans), fit_reg=False)
# x-axis tik scales
tick_locs = [0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.5, 1, 2, 5, 10, 20, 30, 50, 100]
plt.yticks(log_trans(tick_locs), tick_locs)
# labels and title
plt.xlabel('Duration in Minutes')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Duration in Minutes/Distance(km)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON ROW RIGHT: Duration in Hours vs. Ride Distance 
plt.subplot(1,3,3)
# scatter plot 
sb.regplot(x=df['duration_hour'], y=df['distance_km'].apply(log_trans), fit_reg=False)
# x-axis tik scales
tick_locs = [0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.5, 1, 2, 5, 10, 20, 30, 50, 100]
plt.yticks(log_trans(tick_locs), tick_locs)
# labels and title
plt.xlabel('Duration in Hours')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Duration in Hours/Distance(km)", y=1.05, fontsize=14, fontweight='bold');


# check the summary statistics as log type to scale 'distance_km' 
np.log10(ages_minus_0['distance_km']).describe()

C:\Users\sherif\anaconda3\lib\site-packages\pandas\core\series.py:726: RuntimeWarning: divide by zero encountered in log10
  result = getattr(ufunc, method)(*inputs, **kwargs)

count    1.751470e+05
mean             -inf
std               NaN
min              -inf
25%     -4.205788e-02
50%      1.549003e-01
75%      3.467890e-01
max      1.841793e+00
Name: distance_km, dtype: float64


ages_minus_0['distance_km'].describe()

count    175147.000000
mean          1.689329
std           1.096938
min           0.000000
25%           0.907700
50%           1.428566
75%           2.222230
max          69.469241
Name: distance_km, dtype: float64


# plotting scatter plot for  Ride Distance vs. Bike Id (1 Plots)

# transform distances(km) to scale the x axis to the most travelled distancs
def log_trans(y, inverse = False):
    if not inverse:
        return np.log10(y)
    else:
        return np.power(10, y)
    
# figure size
plt.figure(figsize =[20, 8])

# scatter plot 
sb.regplot(x=df['distance_km'].apply(log_trans), y=df['bike_id'], fit_reg=False, scatter_kws={'alpha':1/20})
# x-axis tik scales
tick_locs = [0.01, 0.03, 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, 100]
plt.xticks(log_trans(tick_locs), tick_locs)
# labels and title
plt.xlabel('Ride Distance(km)')
plt.ylabel('Range of Bike Ids)')
plt.xticks(rotation=0)
plt.title("Ride Distance/Bike Ids - Sacatter Plot", y=1.05, fontsize=14, fontweight='bold');


# plot viollin and box plot for User Type vs. Member Age (2 Plot)

""" 
Note: I have used ages_minus_0 subset to plot ages, excluding 0 ages
      to better visualize data distribution
"""
# figure size
plt.figure(figsize =[20, 8])

# VIOLIN PLOT ON LEFT:  User Type vs. Member Age
plt.subplot(1,2,1)
sb.violinplot(data=ages_minus_0, x='user_type', y='member_age', inner='quartile')
           
plt.xlabel('User Type')
plt.ylabel('Member Age(year)')
plt.xticks(rotation=0)
plt.title("User Type/ Member Age - Violin Plot", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT:  User Type vs. Member Age
plt.subplot(1, 2, 2)
sb.boxplot(data=ages_minus_0, x='user_type', y='member_age', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('User Type')
plt.ylabel('Member Age(year)')
plt.xticks(rotation=0)
plt.title("User Type/ Member Age - Box Plot", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for user types/member age
round(ages_minus_0.groupby('user_type')['member_age'].describe())


# plot bar plot for User Type vs. Duration in Seconds, Minutes, and Hours (3 Plots)
# figure size
plt.figure(figsize =[20, 8])


# Bar PLOT ON LEFT:  User Type vs. Duration in Seconds
plt.subplot(1,3,1)
sb.barplot(data=df, x='user_type', y='duration_sec')
plt.xticks(rotation=0)
plt.xlabel('User Type')
plt.ylabel('Avg. Duration(second)')
plt.title("Avg. Duration(second)/User Type", y=1.05, fontsize=14, fontweight='bold');

# Bar PLOT ON MIDDLE:  User Type vs. Duration in minutes
plt.subplot(1, 3, 2)
sb.barplot(data=df, x='user_type', y='duration_minute')
plt.xticks(rotation=0)
plt.xlabel('User Type')
plt.ylabel('Avg. Duratio(minute)')
plt.title("Avg. Duration(minute)/User Type", y=1.05, fontsize=14, fontweight='bold');


# Bar PLOT ON RIGHT:  User Type vs. Duration in hours
plt.subplot(1,3,3)
sb.barplot(data=df, x='user_type', y='duration_hour')
plt.xticks(rotation=0)
plt.xlabel('User Type')
plt.ylabel('Avg. Duration(hour)')
plt.title("Avg. Duration(hour)/User Type", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for user types/durations
df.groupby('user_type')[['duration_sec', 'duration_minute', 'duration_hour']].mean()


# plot box plot for User Type vs. Ride start and End Time Hour and Day(4 Plots)¶
# figure size
plt.figure(figsize =[20, 15])

# BOX PLOT ON LEFT, ROW 1:  User Type vs. Ride start Time Hour
plt.subplot(2,2,1)
sb.boxplot(data=df, x='user_type', y='start_time_hour', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                       "markersize":"10"})
plt.xlabel('User Type')
plt.ylabel('Start Time(hour)')
plt.xticks(rotation=0)
plt.title(("User Type/Start Time(hour)"), y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 1:  User Type vs. Ride End Time Hour
plt.subplot(2,2,2)
sb.boxplot(data=df, x='user_type', y='end_time_hour', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                       "markersize":"10"})
plt.xlabel('User Type')
plt.ylabel('End Time(hour)')
plt.xticks(rotation=0)
plt.title(("User Type/End Time(hour)"), y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON LEFT, ROW 2:  User Type vs. Ride start Time Day
plt.subplot(2,2,3)
sb.boxplot(data=df, x='user_type', y='start_time_day', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                       "markersize":"10"})
plt.xlabel('User Type')
plt.ylabel('Start Time(day)')
plt.xticks(rotation=0)
plt.title(("User Type/Start Time(day)"), y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 2:   User Type vs. Ride start Time Day
plt.subplot(2,2,4)
sb.boxplot(data=df, x='user_type', y='end_time_day', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                       "markersize":"10"})
plt.xlabel('User Type')
plt.ylabel('End Time(day)')
plt.xticks(rotation=0)
plt.title(("User Type/End Time(day)"), y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for user types/start and end time hours and days
df.groupby('user_type')[['start_time_hour', 'end_time_hour', 'start_time_day', 'end_time_day']].mean()


# plot bar plot and box plot for User Type vs. Bike Id and Ride Distance
# figure size
plt.figure(figsize =[20, 15])


# Bar PLOT ON LEFT, ROW 1:  User Type vs. Ride Distance
plt.subplot(2,2,1)
sb.barplot(data=df, x='user_type', y='distance_km')
plt.xticks(rotation=0)
plt.xlabel('User Type')
plt.ylabel('Avg. Distance(km)')
plt.title("Avg.Distance(km)'/User Type - Bar Plot", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 1:  User Type vs. Ride Distance
# transform distances(km) to scale the x axis to the most travelled distancs
def log_trans(y, inverse = False):
    if not inverse:
        return np.log10(y)
    else:
        return np.power(10, y)
    
plt.subplot(2, 2, 2)
sb.boxplot(data=df, x='user_type', y=df['distance_km'].apply(log_trans), showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"20"})
# x-axis tik scales
tick_locs = [0.01, 0.02, 0.03, 0.05, 0.1, 0.2, 0.3, 0.5, 1, 2, 3, 5, 10, 20, 50, 100]
plt.yticks(log_trans(tick_locs), tick_locs)
plt.xticks(rotation=15)
plt.xlabel('User Type')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Distance(km)'/User Type - Box Plot", y=1.05, fontsize=14, fontweight='bold');

# Bar PLOT ON LEFT, ROW 2:  User Type vs. Bike Id 
plt.subplot(2, 2, 3)
sb.barplot(data=df, x='user_type', y='bike_id')
plt.xticks(rotation=0)
plt.xlabel('User Type')
plt.ylabel('Avg. Range of Bike Ids most used')
plt.title("Avg. Range of Bike Ids/User Type", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 2:  User Type vs. Bike Id
plt.subplot(2, 2, 4)
sb.boxplot(data=df, x='user_type', y='bike_id', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('User Type')
plt.ylabel('Bike Ids most used')
plt.xticks(rotation=0)
plt.title("Range of Bike Ids/User Type - Box Plot", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for user types/distance(km) and bike id
df.groupby('user_type')[['distance_km', 'bike_id']].mean()


# plot viollin and box plot for Member Gender vs. Member Age 

""" 
Note: I have used ages_minus_0 subset to plot ages, excluding 0 ages
      to better visualize data distribution
"""

# figure size
plt.figure(figsize =[20, 8])

# VIOLIN PLOT ON LEFT: Member Gender vs. Member Age 
plt.subplot(1,2,1)
sb.violinplot(data=ages_minus_0, x='member_gender', y='member_age', inner='quartile')
           
plt.xlabel('Member Gender')
plt.ylabel('Member Age(year)')
plt.xticks(rotation=0)
plt.title("Member Gender/Member Age - Violin Plot", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON Right:  Member Gender vs. Member Age 
plt.subplot(1, 2, 2)
sb.boxplot(data=ages_minus_0, x='member_gender', y='member_age', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Member Gender')
plt.ylabel('Member Age(year)')
plt.xticks(rotation=0)
plt.title("Member Gender/ Member Age - Box Plot", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for member gender/member age
round(ages_minus_0.groupby('member_gender')['member_age'].mean())

member_gender
Female         33.0
Male           34.0
Not_Defined     NaN
Other          36.0
Name: member_age, dtype: float64


# plot bar plot for Member Gender vs. Duration in Seconds, Minutes, and Hours 
# figure size
plt.figure(figsize =[20, 8])


# Bar PLOT ON LEFT:   Member Gender vs. Duration in Seconds
plt.subplot(1,3,1)
sb.barplot(data=df, x='member_gender', y='duration_sec')
plt.xticks(rotation=0)
plt.xlabel('Member Gender')
plt.ylabel('Avg. Duration(second)')
plt.title("Avg. Duration(second)/Member Gender ", y=1.05, fontsize=14, fontweight='bold');

# Bar PLOT ON MIDDLE:   Member Gender vs. Duration in minutes
plt.subplot(1, 3, 2)
sb.barplot(data=df, x='member_gender', y='duration_minute')
plt.xticks(rotation=0)
plt.xlabel('Member Gender')
plt.ylabel('Avg. Duratio(minute)')
plt.title("Avg. Duration(minute)/Member Gender ", y=1.05, fontsize=14, fontweight='bold');


# Bar PLOT ON RIGHT:   Member Gender vs. Duration in hours
plt.subplot(1,3,3)
sb.barplot(data=df, x='member_gender', y='duration_hour')
plt.xticks(rotation=0)
plt.xlabel('Member Gender')
plt.ylabel('Avg. Duration(hour)')
plt.title("Avg. Duration(hour)/Member Gender ", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for member genders/durations
df.groupby('member_gender')[['duration_sec', 'duration_minute', 'duration_hour']].mean()


# plot box plot for Member Gender vs. Ride Start and End Time Hour and Day 
# figure size
plt.figure(figsize =[20, 15])

# BOX PLOT ON LEFT, ROW 1:  Member Gender vs. Ride start Time Hour
plt.subplot(2,2,1)
sb.boxplot(data=df, x='member_gender', y='start_time_hour', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xlabel('Member Gender')
plt.ylabel('Satrt Time(hour)')
plt.xticks(rotation=0)
plt.title("Member Gender/Satrt Time(hour)", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 1:  Member Gender vs. Ride End Time Hour
plt.subplot(2,2,2)
sb.boxplot(data=df, x='member_gender', y='end_time_hour', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xlabel('Member Gender')
plt.ylabel('End Time(hour)')
plt.xticks(rotation=0)
plt.title("Member Gender/End Time(hour)", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON LEFT, ROW 2:  Member Gender vs. Ride start Time  Day
plt.subplot(2,2,3)
sb.boxplot(data=df, x='member_gender', y='start_time_day', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xlabel('Member Gender')
plt.ylabel('Satrt Time(day)')
plt.xticks(rotation=0)
plt.title("Member Gender/Start Time(day)", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 2:   Member Gender vs. Ride End Time Day
plt.subplot(2,2,4)
sb.boxplot(data=df, x='member_gender', y='end_time_day', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xlabel('Member Gender')
plt.ylabel('End Time(day)')
plt.xticks(rotation=0)
plt.title("Member Gender/End Time(day)", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for member genders/start and end time hour and day
df.groupby('member_gender')[['start_time_hour', 'end_time_hour','start_time_day','end_time_day']].mean()


# plot bar plot and box plot for Member Gender vs. Bike Id and Ride Distance
# figure size
plt.figure(figsize =[20, 15])

# Bar PLOT ON LEFT, ROW 1:  Member Gender vs. Ride Distance
plt.subplot(2,2,1)
sb.barplot(data=df, x='member_gender', y='distance_km')
plt.xticks(rotation=0)
plt.xlabel('Member Gender')
plt.ylabel('Avg. Distance(km)')
plt.title("Avg.Distance(km)'/Member Gender - Bar Plot", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 1:  Member Gender vs. Ride Distance
# transform distances(km) to scale the x axis to the most travelled distancs
def log_trans(y, inverse = False):
    if not inverse:
        return np.log10(y)
    else:
        return np.power(10, y)
    
plt.subplot(2, 2, 2)
sb.boxplot(data=df, x='member_gender', y=df['distance_km'].apply(log_trans), showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"20"})
# x-axis tik scales
tick_locs = [0.01, 0.02, 0.03, 0.05, 0.1, 0.2, 0.3, 0.5, 1, 2, 3, 5, 10, 20, 50, 100]
plt.yticks(log_trans(tick_locs), tick_locs)
plt.xticks(rotation=15)
plt.xlabel('Member Gender')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Distance(km)'/Member Gender - Box Plot", y=1.05, fontsize=14, fontweight='bold');

# Bar PLOT ON LEFT, ROW 2:  Member Gender vs. Bike Id 
plt.subplot(2, 2, 3)
sb.barplot(data=df, x='member_gender', y='bike_id')
plt.xticks(rotation=0)
plt.xlabel('Member Gender')
plt.ylabel('Avg. Range of Bike Ids most used')
plt.title("Avg. Range of Bike Ids/Member Gender", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 2:  Member Gender vs. Bike Id
plt.subplot(2, 2, 4)
sb.boxplot(data=df, x='member_gender', y='bike_id', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Member Gender')
plt.ylabel('Bike Ids most used')
plt.xticks(rotation=0)
plt.title("Range of Bike Ids/Member Gender - Box Plot", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for member genders/distance and bike id
df.groupby('member_gender')[['distance_km', 'bike_id']].mean()


# plot viollin, box plot, and bar plot for Bike Share Status vs. Member Age 

""" 
Note: I have used ages_minus_0 subset to plot ages, excluding 0 ages
      to better visualize data distribution
"""
# figure size
plt.figure(figsize =[20, 8])

# VIOLIN PLOT ON LEFT: Bike Share Status vs. Member Age 
plt.subplot(1, 3, 1)
sb.violinplot(data=ages_minus_0, x='bike_share_for_all_trip', y='member_age', inner='quartile')
           
plt.xlabel('Bike Share Status')
plt.ylabel('Member Age(year)')
plt.xticks(rotation=0)
plt.title("Bike Share Status/Member Age - Violin Plot", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON MIDDLE:  Bike Share Status vs. Member Age 
plt.subplot(1, 3, 2)
sb.boxplot(data=ages_minus_0, x='bike_share_for_all_trip', y='member_age', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Bike Share Status')
plt.ylabel('Member Age(year)')
plt.xticks(rotation=0)
plt.title("Bike Share Status/Member Age - Box Plot", y=1.05, fontsize=14, fontweight='bold');


# Bar PLOT ON RIGHT: Bike Share Status vs. Member Age 
plt.subplot(1, 3, 3)
sb.barplot(data=ages_minus_0, x='bike_share_for_all_trip', y='member_age')
plt.xticks(rotation=0)
plt.xlabel('Bike Share Status')
plt.ylabel('Member Age(year)')
plt.title("Bike Share Status/Member Age - Bar Plot", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for bike share status/member age
round(ages_minus_0.groupby('bike_share_for_all_trip')['member_age'].mean())

bike_share_for_all_trip
No     34.0
Yes    32.0
Name: member_age, dtype: float64


# plot box plot for Bike Share Status vs. Duration in Seconds, Minutes, and Hours
# figure size
plt.figure(figsize =[20, 8])

# transform y-axis
def log_trans(y, inverse = False):
    if not inverse:
        return np.log10(y)
    else:
        return np.power(10, y)
    

# BOX PLOT ON LEFT, ROW 1:  Bike Share Status vs. Duration in Seconds
plt.subplot(1,3,1)
sb.boxplot(data=df, x='bike_share_for_all_trip', y=df['duration_sec'].apply(log_trans), showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                       "markersize":"10"})
# y-axis tik scales
tick_locs = [1,10, 50, 100, 300, 500, 700, 1000, 3000, 5000, 10000, 20000, 30000, 50000, 100000]
plt.yticks(log_trans(tick_locs), tick_locs)
plt.xlabel('Bike Share Status')
plt.ylabel('Duration(second)')
plt.xticks(rotation=0)
plt.title(("Bike Share Status/Duration(second)"), y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON MIDDLE, ROW 1:  Bike Share Status vs. Duration in Minutes
plt.subplot(1,3,2)
sb.boxplot(data=df, x='bike_share_for_all_trip', y=df['duration_minute'].apply(log_trans), showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                       "markersize":"10"})
# y-axis tik scales
tick_locs = [1, 3, 5, 7, 10, 15, 20, 30, 50, 100, 300, 500, 1000, 1500]
plt.yticks(log_trans(tick_locs), tick_locs)
plt.xlabel('Bike Share Status')
plt.ylabel('Duration(minute)')
plt.xticks(rotation=0)
plt.title(("Bike Share Status/Duration(minute)"), y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 2:  Bike Share Status vs. Duration in Hours
plt.subplot(1,3,3)
sb.boxplot(data=df, x='bike_share_for_all_trip', y=df['duration_hour'].apply(log_trans), showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                       "markersize":"10"})
# y-axis tik scales
tick_locs = [0.001, 0.005, 0.01, 0.02, 0.03, 0.05, 0.07, 0.1, 0.2, 0.3, 0.5, 1, 3, 5, 10, 15, 20, 25]
plt.yticks(log_trans(tick_locs), tick_locs)
plt.xlabel('Bike Share Status')
plt.ylabel('Duration(hour)')
plt.xticks(rotation=0)
plt.title(("Bike Share Status/Duration(hour)"), y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for Bike Share Status vs. Duration in Seconds, Minutes, and Hours
df.groupby('bike_share_for_all_trip')[['duration_sec', 'duration_minute', 'duration_hour']].mean()


# plot box plot for Bike Share Status vs. Ride Start and End Time Hour and Day
# figure size
plt.figure(figsize =[20, 15])

# BOX PLOT ON LEFT, ROW 1:  Bike Share Status vs. Ride Start Time Hour
plt.subplot(2,2,1)
sb.boxplot(data=df, x='bike_share_for_all_trip', y='start_time_hour', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xlabel('Bike Share Status')
plt.ylabel('Satrt Time(hour)')
plt.xticks(rotation=0)
plt.title("Bike Share Status/Satrt Time(hour)", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 1:  Bike Share Status vs. Ride End Time Hour 
plt.subplot(2,2,2)
sb.boxplot(data=df, x='bike_share_for_all_trip', y='end_time_hour', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xlabel('Bike Share Status')
plt.ylabel('End Time(hour)')
plt.xticks(rotation=0)
plt.title("Bike Share Status/End Time(hour)", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON LEFT, ROW 2:  Bike Share Status vs. Ride Start Time Day
plt.subplot(2,2,3)
sb.boxplot(data=df, x='bike_share_for_all_trip', y='start_time_day', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xlabel('Bike Share Status')
plt.ylabel('Satrt Time(hour)')
plt.xticks(rotation=0)
plt.title("Bike Share Status/Start Time(day)", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 2:  Bike Share Status vs. Ride End Time Day
plt.subplot(2,2,4)
sb.boxplot(data=df, x='bike_share_for_all_trip', y='end_time_day', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xlabel('Bike Share Status')
plt.ylabel('End Time(day)')
plt.xticks(rotation=0)
plt.title("Bike Share Status/End Time(day)", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for Bike Share Status vs. Ride Start and End Time Hour and Day
df.groupby('bike_share_for_all_trip')[['start_time_hour', 'end_time_hour', 'start_time_day', 'end_time_day']].mean()


# plot bar plot and box plot for Bike Share Status vs. Bike Id and Ride Distance 
# figure size
plt.figure(figsize =[20, 15])

# Bar PLOT ON LEFT, ROW 1:  Bike Share Status vs. Ride Distance
plt.subplot(2,2,1)
sb.barplot(data=df, x='bike_share_for_all_trip', y='distance_km')
plt.xticks(rotation=0)
plt.xlabel('Bike Share Status')
plt.ylabel('Avg. Distance(km)')
plt.title("Avg.Distance(km)'/Bike Share Status - Bar Plot", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 1:  Bike Share Status vs. Ride Distance
# transform distances(km) to scale the x axis to the most travelled distancs
def log_trans(y, inverse = False):
    if not inverse:
        return np.log10(y)
    else:
        return np.power(10, y)
    
plt.subplot(2, 2, 2)
sb.boxplot(data=df, x='bike_share_for_all_trip', y=df['distance_km'].apply(log_trans), showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"20"})
# x-axis tik scales
tick_locs = [0.01, 0.02, 0.03, 0.05, 0.1, 0.2, 0.3, 0.5, 1, 2, 3, 5, 10, 20, 50, 100]
plt.yticks(log_trans(tick_locs), tick_locs)
plt.xticks(rotation=15)
plt.xlabel('Bike Share Status')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Distance(km)'/Bike Share Status - Box Plot", y=1.05, fontsize=14, fontweight='bold');

# Bar PLOT ON LEFT, ROW 2:  Bike Share Status vs. Bike Id 
plt.subplot(2, 2, 3)
sb.barplot(data=df, x='bike_share_for_all_trip', y='bike_id')
plt.xticks(rotation=0)
plt.xlabel('Bike Share Status')
plt.ylabel('Avg. Range of Bike Ids most used')
plt.title("Avg. Range of Bike Ids/Bike Share Status", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 2:  Bike Share Status vs. Bike Id
plt.subplot(2, 2, 4)
sb.boxplot(data=df, x='bike_share_for_all_trip', y='bike_id', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Bike Share Status')
plt.ylabel('Bike Ids most used')
plt.xticks(rotation=0)
plt.title("Range of Bike Ids/Bike Share Status - Box Plot", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for Bike Share Status vs. Bike Id and Ride Distance
df.groupby('bike_share_for_all_trip')[['distance_km', 'bike_id']].mean()


# plot box plot for Ride Stations vs. Member Age 
# figure size
plt.figure(figsize =[20, 8])

# BOX PLOT ON LEFT, ROW 1:  San_Fransisco Ride Stations vs. Member Age 
plt.subplot(1,3,1)
# plot box plot
sb.boxplot(data=San_Fransisco, x='start_station_name', y='member_age', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Member Ages/San_Fransisco Start Stations", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON MIDDLE, ROW 1:  Oakland_Berkeley Ride Stations vs. Member Age 
plt.subplot(1,3,2)
# plot box plot
sb.boxplot(data=Oakland_Berkeley, x='start_station_name', y='member_age', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Member Ages/Oakland_Berkeley Start Stations", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 1:  San_Jose Ride Stations vs. Member Age 
plt.subplot(1,3,3)
# plot box plot
sb.boxplot(data=San_Jose, x='start_station_name', y='member_age', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Member Ages/San_Jose Start Stations", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for Bike Share Status vs. Bike Id and Ride Distance
print("Average Member Age for San Fransisco 's Sations: {}".format(round(San_Fransisco.groupby('start_station_name')
                                                                         ['member_age'].mean().mean())))
print("Average Member Age for Oakland_Berkeley 's Sations: {}".format(round(Oakland_Berkeley.groupby('start_station_name')
                                                                            ['member_age'].mean().mean())))
print("Average Member Age for San Jose 's Sations: {}".format(round(San_Jose.groupby('start_station_name')
                                                                    ['member_age'].mean().mean())))

Average Member Age for San Fransisco 's Sations: 33
Average Member Age for Oakland_Berkeley 's Sations: 34
Average Member Age for San Jose 's Sations: 31


# plot box plot for Ride Stations vs. Duration in Seconds, Minutes, and Hours
# figure size
plt.figure(figsize =[20, 20])

# BOX PLOT ON LEFT, ROW 1:  San_Fransisco Ride Stations vs. Duration in Seconds
plt.subplot(3,3,1)
# plot box plot
sb.boxplot(data=San_Fransisco, x='start_station_name', y='duration_sec', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Duration(second)/San_Fransisco Start Stations", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON MIDDLE, ROW 1:  San_Fransisco Ride Stations vs. Duration in Minutes
plt.subplot(3,3,2)
# plot box plot
sb.boxplot(data=San_Fransisco, x='start_station_name', y='duration_minute', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Duration(minute)/San_Fransisco Start Stations", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 1: San_Fransisco Ride Stations vs. Duration in Hours
plt.subplot(3,3,3)
# plot box plot
sb.boxplot(data=San_Fransisco, x='start_station_name', y='duration_hour', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Duration(hour)/San_Fransisco Start Stations", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON LEFT, ROW 2:  Oakland_Berkeley Ride Stations vs. Duration in Seconds
plt.subplot(3,3,4)
# plot box plot
sb.boxplot(data=Oakland_Berkeley, x='start_station_name', y='duration_sec', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Duration(second)/Oakland_Berkeley Start Stations", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON MIDDLE, ROW 2:  Oakland_Berkeley Ride Stations vs. Duration in Minutes
plt.subplot(3,3,5)
# plot box plot
sb.boxplot(data=Oakland_Berkeley, x='start_station_name', y='duration_minute', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Duration(minute)/Oakland_Berkeley Start Stations", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 2:  Oakland_Berkeley Ride Stations vs. Duration Hours
plt.subplot(3,3,6)
# plot box plot
sb.boxplot(data=Oakland_Berkeley, x='start_station_name', y='duration_hour', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Duration(hour)/Oakland_Berkeley Start Stations", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON LEFT, ROW 3:  San_Jose Ride Stations vs. Duration in Seconds
plt.subplot(3,3,7)
# plot box plot
sb.boxplot(data=San_Jose, x='start_station_name', y='duration_sec', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Duration(second)/San_Jose Start Stations", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON MIDDLE, ROW 3:  San_Jose Ride Stations vs. Duration in Minutes
plt.subplot(3,3,8)
# plot box plot
sb.boxplot(data=San_Jose, x='start_station_name', y='duration_minute', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Duration(minute)/San_Jose Start Stations", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 3:  San_Jose Ride Stations vs. Duration in Hours
plt.subplot(3,3,9)
# plot box plot
sb.boxplot(data=San_Jose, x='start_station_name', y='duration_hour', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Duration(hour)/San_Jose Start Stations", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for Ride Stations vs. Duration in Seconds, Minutes, and Hours
print("Average Duration (second) for San Fransisco 's Sations: {}".format(San_Fransisco.groupby('start_station_name')
                                                                          ['duration_sec'].mean().mean()))
print("Average Duration (minute) for San Fransisco 's Sations: {}".format(San_Fransisco.groupby('start_station_name')
                                                                          ['duration_minute'].mean().mean()))
print("Average Duration (hour) for San Fransisco 's Sations: {}".format(San_Fransisco.groupby('start_station_name')
                                                                        ['duration_hour'].mean().mean()))
print("Average Duration (second) for Oakland_Berkeley 's Sations: {}".format(Oakland_Berkeley.groupby('start_station_name')
                                                                             ['duration_sec'].mean().mean()))
print("Average Duration (minute) for Oakland_Berkeley 's Sations: {}".format(Oakland_Berkeley.groupby('start_station_name')
                                                                             ['duration_minute'].mean().mean()))
print("Average Duration (hour) for Oakland_Berkeley 's Sations: {}".format(Oakland_Berkeley.groupby('start_station_name')
                                                                             ['duration_hour'].mean().mean()))
print("Average Duration (second) for San Jose's Sations: {}".format(San_Jose.groupby('start_station_name')
                                                                    ['duration_sec'].mean().mean()))
print("Average Duration (minute) for San Jose's Sations: {}".format(San_Jose.groupby('start_station_name')
                                                                    ['duration_minute'].mean().mean()))
print("Average Duration (hour) for San Jose's Sations: {}".format(San_Jose.groupby('start_station_name')
                                                                    ['duration_hour'].mean().mean()))

Average Duration (second) for San Fransisco 's Sations: 812.576605692139
Average Duration (minute) for San Fransisco 's Sations: 13.54292960909751
Average Duration (hour) for San Fransisco 's Sations: 0.22571734196102256
Average Duration (second) for Oakland_Berkeley 's Sations: 747.5424529127471
Average Duration (minute) for Oakland_Berkeley 's Sations: 12.45902643751609
Average Duration (hour) for Oakland_Berkeley 's Sations: 0.2076423657527583
Average Duration (second) for San Jose's Sations: 752.5631162273226
Average Duration (minute) for San Jose's Sations: 12.542606416313205
Average Duration (hour) for San Jose's Sations: 0.2090948373619982


# plot box plot for  Ride stations vs. Ride Start Time Hour and Day 
# figure size
plt.figure(figsize =[20, 20])

# BOX PLOT ON LEFT, ROW 1:  San_Fransisco Ride Stations vs. Ride Start Time Hour
plt.subplot(3,2,1)
# plot box plot
sb.boxplot(data=San_Fransisco, x='start_station_name', y='start_time_hour', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Ride Start Time(hour)/San_Fransisco Start Stations", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 1:  San_Fransisco Ride Stations vs. Ride Start Time day
plt.subplot(3,2,2)
# plot box plot
sb.boxplot(data=San_Fransisco, x='start_station_name', y='start_time_day', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Ride Start Time(day)/San_Fransisco Start Stations", y=1.05, fontsize=14, fontweight='bold');


# BOX PLOT ON LEFT, ROW 2:  Oakland_Berkeley Ride Stations vs. Ride Start Time hour
plt.subplot(3,2,3)
# plot box plot
sb.boxplot(data=Oakland_Berkeley, x='start_station_name', y='start_time_hour', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Ride Start Time(hour)/Oakland_Berkeley Start Stations", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 2:  Oakland_Berkeley Ride Stations vs.Ride Start Time day
plt.subplot(3,2,4)
# plot box plot
sb.boxplot(data=Oakland_Berkeley, x='start_station_name', y='start_time_day', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Ride Start Time(day)/Oakland_Berkeley Start Stations", y=1.05, fontsize=14, fontweight='bold');


# BOX PLOT ON LEFT, ROW 3:  San_Jose Ride Stations vs. Ride Start Time hour
plt.subplot(3,2,5)
# plot box plot
sb.boxplot(data=San_Jose, x='start_station_name', y='start_time_hour', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Ride Start Time(hour)/San_Jose Start Stations", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 3:  San_Jose Ride Stations vs. Ride Start Time day
plt.subplot(3,2,6)
# plot box plot
sb.boxplot(data=San_Jose, x='start_station_name', y='start_time_day', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Ride Start Time(day)/San_Jose Start Stations", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for Ride Stations vs. Duration in Seconds, Minutes, and Hours
print("Average start time(hour) for San Fransisco 's Sations: {}".format(San_Fransisco.groupby('start_station_name')
                                                                          ['start_time_hour'].mean().mean()))
print("Average  start time(day) for San Fransisco 's Sations: {}".format(San_Fransisco.groupby('start_station_name')
                                                                          ['start_time_day'].mean().mean()))
print("Average start time(hour) for Oakland_Berkeley 's Sations: {}".format(Oakland_Berkeley.groupby('start_station_name')
                                                                             ['start_time_hour'].mean().mean()))
print("Average  start time(day) for Oakland_Berkeley 's Sations: {}".format(Oakland_Berkeley.groupby('start_station_name')
                                                                             ['start_time_day'].mean().mean()))
print("Average start time(hour) for San Jose's Sations: {}".format(San_Jose.groupby('start_station_name')
                                                                    ['start_time_hour'].mean().mean()))
print("Average start time(day) for San Jose's Sations: {}".format(San_Jose.groupby('start_station_name')
                                                                    ['start_time_day'].mean().mean()))

Average start time(hour) for San Fransisco 's Sations: 13.093916467528285
Average  start time(day) for San Fransisco 's Sations: 15.55885356565615
Average start time(hour) for Oakland_Berkeley 's Sations: 13.049649423067937
Average  start time(day) for Oakland_Berkeley 's Sations: 14.932767174032692
Average start time(hour) for San Jose's Sations: 14.216047341995823
Average start time(day) for San Jose's Sations: 15.65265453308485


# plot box plot for Ride stations vs. Bike Id and Ride Distance
# figure size
plt.figure(figsize =[20, 20])

# BOX PLOT ON LEFT, ROW 1:  San_Fransisco Ride Stations vs. Ride Distance
plt.subplot(3,2,1)
# plot box plot
sb.boxplot(data=San_Fransisco, x='start_station_name', y='distance_km', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Distance(km)/San_Fransisco Start Stations", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 1:  San_Fransisco Ride Stations vs. Bike Id
plt.subplot(3,2,2)
# plot box plot
sb.boxplot(data=San_Fransisco, x='start_station_name', y='bike_id', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Bike Id/San_Fransisco Start Stations", y=1.05, fontsize=14, fontweight='bold');


# BOX PLOT ON LEFT, ROW 2:  Oakland_Berkeley Ride Stations vs. Ride Distance
plt.subplot(3,2,3)
# plot box plot
sb.boxplot(data=Oakland_Berkeley, x='start_station_name', y='distance_km', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Distance(km)/Oakland_Berkeley Start Stations", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 2:  Oakland_Berkeley Ride Stations vs. Bike Id
plt.subplot(3,2,4)
# plot box plot
sb.boxplot(data=Oakland_Berkeley, x='start_station_name', y='bike_id', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Bike Id/Oakland_Berkeley Start Stations", y=1.05, fontsize=14, fontweight='bold');


# BOX PLOT ON LEFT, ROW 3:  San_Jose Ride Stations vs. Ride Distance
plt.subplot(3,2,5)
# plot box plot
sb.boxplot(data=San_Jose, x='start_station_name', y='distance_km', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Distance(km)/San_Jose Start Stations", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 3:  San_Jose Ride Stations vs. Bike Id
plt.subplot(3,2,6)
# plot box plot
sb.boxplot(data=San_Jose, x='start_station_name', y='bike_id', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Station Name')
plt.ylabel('Member Ages')
plt.xticks(rotation=90)
plt.title("Bike Id/San_Jose Start Stations", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for Ride stations vs. Bike Id and Ride Distance
print("Average distance(km) for San Fransisco 's Sations: {}".format(San_Fransisco.groupby('start_station_name')
                                                                          ['distance_km'].mean().mean()))
print("Average  bike id used for San Fransisco 's Sations: {}".format(San_Fransisco.groupby('start_station_name')
                                                                          ['bike_id'].mean().mean()))
print("Average distance(km) for Oakland_Berkeley 's Sations: {}".format(Oakland_Berkeley.groupby('start_station_name')
                                                                             ['distance_km'].mean().mean()))
print("Average  bike id used for Oakland_Berkeley 's Sations: {}".format(Oakland_Berkeley.groupby('start_station_name')
                                                                             ['bike_id'].mean().mean()))
print("Average distance(km) for San Jose's Sations: {}".format(San_Jose.groupby('start_station_name')
                                                                    ['distance_km'].mean().mean()))
print("Average bike id used for San Jose's Sations: {}".format(San_Jose.groupby('start_station_name')
                                                                    ['bike_id'].mean().mean()))

Average distance(km) for San Fransisco 's Sations: 1.9088890663755218
Average  bike id used for San Fransisco 's Sations: 4650.979050043828
Average distance(km) for Oakland_Berkeley 's Sations: 1.664937677411198
Average  bike id used for Oakland_Berkeley 's Sations: 4104.266749973715
Average distance(km) for San Jose's Sations: 1.1625837724897017
Average bike id used for San Jose's Sations: 3763.5519534538594


# plot box plot for Weekday vs. Member Age

""" 
Note: I have used ages_minus_0 subset to plot ages, excluding 0 ages
      to better visualize data distribution
"""
# figure size
plt.figure(figsize =[20, 8]) 

# VIOLIN PLOT ON LEFT, ROW 1:  Start Weekday vs. Member Age 
plt.subplot(1, 2, 1)
sb.violinplot(data=ages_minus_0, x='start_time_weekday', y='member_age', inner='quartile')
plt.xticks(rotation=15)
plt.xlabel('Start Weekday')
plt.ylabel('Member Age(year)')
plt.xticks(rotation=0)
plt.title("Start Weekday/Member Age - Violin Plot", y=1.05, fontsize=14, fontweight='bold');


# VIOLIN PLOT ON RIGHT, ROW 1:  End Weekday vs. Member Age
plt.subplot(1, 2, 2)
sb.violinplot(data=ages_minus_0, x='end_time_weekday', y='member_age', inner='quartile')
plt.xticks(rotation=0)
plt.xlabel('End Weekday')
plt.ylabel('Member Age(year)')
plt.title("End Weekday/Member Age - Violin Plot", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for Weekday vs. Member Age
print("Average age per start weekday: {}".format(round(ages_minus_0.groupby('start_time_weekday')['member_age'].mean())))
print("Average age per end weekday: {}".format(round(ages_minus_0.groupby('end_time_weekday')['member_age'].mean())))

Average age per start weekday: start_time_weekday
Friday       34.0
Monday       34.0
Saturday     33.0
Sunday       33.0
Thursday     35.0
Tuesday      34.0
Wednesday    34.0
Name: member_age, dtype: float64
Average age per end weekday: end_time_weekday
Friday       34.0
Monday       34.0
Saturday     33.0
Sunday       33.0
Thursday     35.0
Tuesday      34.0
Wednesday    34.0
Name: member_age, dtype: float64


# plot viollin plot Weekday vs. Duration in Seconds, Minutes, and Hours

# figure size
plt.figure(figsize =[20, 8])

def log_trans(y, inverse = False):
    if not inverse:
        return np.log10(y)
    else:
        return np.power(10, y)


# BOX PLOT ON LEFT, ROW 1: Start Weekday vs. Duration in Seconds
plt.subplot(1,3,1)
sb.boxplot(data=df, x='start_time_weekday', y=df['duration_sec'].apply(log_trans), showmeans=True, 
            meanprops={"marker":"o", "markerfacecolor":"white", "markersize":"10"})
# y-axis tik scales
tick_locs = [1,10, 50, 100, 300, 500, 700, 1000, 3000, 5000, 10000, 20000, 30000, 50000, 100000]
plt.yticks(log_trans(tick_locs), tick_locs)
plt.xlabel('Start Weekday')
plt.ylabel('Duration(second)')
plt.xticks(rotation=30)
plt.title("Start Weekday/Duration(second)", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON MIDDLE, ROW 1: Start Weekday vs. Duration in Minutes 
plt.subplot(1,3,2)
sb.boxplot(data=df, x='start_time_weekday', y=df['duration_minute'].apply(log_trans), showmeans=True,  
           meanprops={"marker":"o", "markerfacecolor":"white", "markersize":"10"}) 
# y-axis tik scales
tick_locs = [1, 3, 5, 7, 10, 15, 20, 30, 50, 100, 300, 500, 1000, 1500]
plt.yticks(log_trans(tick_locs), tick_locs)
plt.xticks(rotation=30)
plt.xlabel('Start Weekday')
plt.ylabel('Duration(munite)')
plt.title("Start Weekday/Duration(minute)", y=1.05, fontsize=14, fontweight='bold');


# BOX PLOT ON RIGHT, ROW 1: Start Weekday vs. Duration in Hours
plt.subplot(1,3,3)
sb.boxplot(data=df, x='start_time_weekday', y=df['duration_hour'].apply(log_trans), showmeans=True,  
           meanprops={"marker":"o", "markerfacecolor":"white", "markersize":"10"})
# y-axis tik scales
tick_locs = [0.001, 0.005, 0.01, 0.02, 0.03, 0.05, 0.07, 0.1, 0.2, 0.3, 0.5, 1, 3, 5, 10, 15, 20, 25]
plt.yticks(log_trans(tick_locs), tick_locs)
plt.xticks(rotation=30)
plt.xlabel('Start Weekday')
plt.ylabel('Duration(hour)')
plt.title("Start Weekday/Duration(hour)", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for  Weekday vs. Duration in Seconds, Minutes, and Hours
df.groupby('start_time_weekday')[['duration_sec', 'duration_minute', 'duration_hour']].mean()


# plot box plot for Weekday vs. Ride start and End Time Hour

# figure size
plt.figure(figsize =[20, 16])

# BOX PLOT ON LEFT, ROW 1: Start Weekday vs. Start time Hour
plt.subplot(2,2,1)
sb.boxplot(data=df, x='start_time_weekday', y=df['start_time_hour'], showmeans=True, 
            meanprops={"marker":"o", "markerfacecolor":"white", "markersize":"10"})
plt.xlabel('Start Weekday')
plt.ylabel('Start Time(hour)')
plt.xticks(rotation=0)
plt.title("Start Weekday/Start Time(hour)", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 1: Start Weekday vs. end time hour
plt.subplot(2,2,2)
sb.boxplot(data=df, x='start_time_weekday', y=df['end_time_hour'], showmeans=True, 
            meanprops={"marker":"o", "markerfacecolor":"white", "markersize":"10"})
plt.xlabel('End Weekday')
plt.ylabel('Start Time(hour)')
plt.xticks(rotation=0)
plt.title("Start Weekday/End Time(hour)", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON LEFT, ROW 2: End Weekday vs. start time day
plt.subplot(2,2,3)
sb.boxplot(data=df, x='start_time_weekday', y=df['start_time_day'], showmeans=True, 
            meanprops={"marker":"o", "markerfacecolor":"white", "markersize":"10"})
plt.xticks(rotation=15)
plt.xlabel('Start Weekday')
plt.ylabel('Start Time(day')
plt.xticks(rotation=0)
plt.title("Start Weekday/Start Time(day)", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 2: End Weekday vs. End time day
plt.subplot(2,2,4)
sb.boxplot(data=df, x='start_time_weekday', y=df['end_time_day'], showmeans=True, 
            meanprops={"marker":"o", "markerfacecolor":"white", "markersize":"10"})
plt.xticks(rotation=30)
plt.xlabel('End Weekday')
plt.ylabel('End Time(day')
plt.xticks(rotation=0)
plt.title("Start Weekday/End Time(day)", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for  Weekday vs. Ride start and End Time Hour and Day
df.groupby('start_time_weekday')[['start_time_hour', 'end_time_hour', 'start_time_day','end_time_day']].mean()


# plot bar plot and box plot for Start Weekday vs. Bike Id and Ride Distance
# figure size
plt.figure(figsize =[20, 15])

# Bar PLOT ON LEFT, ROW 1:  Start Weekday vs. Ride Distance
plt.subplot(2,2,1)
sb.barplot(data=df, x='start_time_weekday', y='distance_km')
plt.xticks(rotation=0)
plt.xlabel(' Start Weekday')
plt.ylabel('Avg. Distance(km)')
plt.title("Avg.Distance(km)'/Start Weekday", y=1.05, fontsize=14, fontweight='bold');

# Bar PLOT ON LEFT, ROW 1:  End Weekday vs. Ride Distance
plt.subplot(2,2,2)
sb.barplot(data=df, x='end_time_weekday', y='distance_km')
plt.xticks(rotation=0)
plt.xlabel(' End Weekday')
plt.ylabel('Avg. Distance(km)')
plt.title("Avg.Distance(km)'/End Weekday", y=1.05, fontsize=14, fontweight='bold');


# Bar PLOT ON RIGHT, ROW 1:  Start Weekday vs. Bike Id 
plt.subplot(2, 2, 3)
sb.barplot(data=df, x='start_time_weekday', y='bike_id')
plt.xticks(rotation=0)
plt.xlabel('Start Weekday')
plt.ylabel('Avg. Range of Bike Ids most used')
plt.title("Avg. Range of Bike Ids/Start Weekday", y=1.05, fontsize=14, fontweight='bold');


# Bar PLOT ON RIGHT, ROW 1:  End Weekday vs. Bike Id 
plt.subplot(2, 2, 4)
sb.barplot(data=df, x='end_time_weekday', y='bike_id')
plt.xticks(rotation=0)
plt.xlabel('End Weekday')
plt.ylabel('Avg. Range of Bike Ids most used')
plt.title("Avg. Range of Bike Ids/End Weekday", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for  Weekday vs. Ride start and End Time Hour and Day
(df.groupby('start_time_weekday')[['distance_km', 'bike_id']].mean(), 
df.groupby('end_time_weekday')[['distance_km', 'bike_id']].mean())

(                    distance_km      bike_id
 start_time_weekday                          
 Friday                 1.721757  4433.640696
 Monday                 1.673051  4380.645650
 Saturday               1.608491  4628.002207
 Sunday                 1.608656  4575.100496
 Thursday               1.724219  4490.056681
 Tuesday                1.712082  4400.652249
 Wednesday              1.699010  4517.935697,
                   distance_km      bike_id
 end_time_weekday                          
 Friday               1.722522  4434.009420
 Monday               1.672490  4380.701255
 Saturday             1.609141  4627.180432
 Sunday               1.608401  4576.550860
 Thursday             1.723960  4490.638170
 Tuesday              1.712113  4399.857480
 Wednesday            1.698878  4517.374439)


# plot box plot for Age groups vs. Duration in Seconds, Minutes, and Hours 

# figure size
plt.figure(figsize =[20, 8])

def log_trans(y, inverse = False):
    if not inverse:
        return np.log10(y)
    else:
        return np.power(10, y)


# BOX PLOT ON LEFT, ROW 1: Age groups vs. Duration in Seconds
plt.subplot(1,3,1)
sb.boxplot(data=df, x='net_age_groups', y=df['duration_sec'].apply(log_trans), showmeans=True,  
           meanprops={"marker":"o", "markerfacecolor":"white", "markersize":"10"}) 
# y-axis tik scales
tick_locs = [10, 50, 100, 300, 500, 700, 1000, 3000, 5000, 10000, 20000, 30000, 50000, 100000]
plt.yticks(log_trans(tick_locs), tick_locs)
plt.xlabel('Age Group')
plt.ylabel('Duration(second)')
plt.xticks(rotation=0)
plt.title("Age Group/Duration(second)", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON MIDDLE, ROW 1: Age groups vs. Duration in Minutes 
plt.subplot(1,3,2)
sb.boxplot(data=df, x='net_age_groups', y=df['duration_minute'].apply(log_trans), showmeans=True,  
           meanprops={"marker":"o", "markerfacecolor":"white", "markersize":"10"}) 
# y-axis tik scales
tick_locs = [1, 3, 5, 8, 10, 15, 20, 30, 50, 100, 300, 500, 1000, 1500]
plt.yticks(log_trans(tick_locs), tick_locs)
plt.xticks(rotation=0)
plt.xlabel('Age Group')
plt.ylabel('Duration(munite)')
plt.title("Age Group/Duration(minute)", y=1.05, fontsize=14, fontweight='bold');


# BOX PLOT ON RIGHT, ROW 1: Age groups vs. Duration in Hours
plt.subplot(1,3,3)
sb.boxplot(data=df, x='net_age_groups', y=df['duration_hour'].apply(log_trans), showmeans=True,  
           meanprops={"marker":"o", "markerfacecolor":"white", "markersize":"10"})
# y-axis tik scales
tick_locs = [0.001, 0.005, 0.01, 0.02, 0.03, 0.05, 0.08, 0.1, 0.15, 0.2, 0.3, 0.5, 1, 3, 5, 10, 15, 20, 25]
plt.yticks(log_trans(tick_locs), tick_locs)
plt.xticks(rotation=0)
plt.xlabel('Age Group')
plt.ylabel('Duration(hour)')
plt.title("Age Group/Duration(hour)", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for  Age groups vs. Duration in Seconds, Minutes, and Hours 
df.groupby('net_age_groups')[['duration_sec', 'duration_minute', 'duration_hour']].mean()


# plot box plot for Age groups vs. Ride start and End Time Hour and Day

# figure size
plt.figure(figsize =[20, 15])

# BOX PLOT ON LEFT, ROW 1:  Age groups vs. Ride Start Time Hour
plt.subplot(2,2,1)
sb.boxplot(data=df, x='net_age_groups', y='start_time_hour', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xlabel('Age group')
plt.ylabel('Satrt Time(hour)')
plt.xticks(rotation=0)
plt.title("Age groups/Satrt Time(hour)", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 1:  Age groups vs. Ride End Time Hour 
plt.subplot(2,2,2)
sb.boxplot(data=df, x='net_age_groups', y='end_time_hour', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xlabel('Age group')
plt.ylabel('End Time(hour)')
plt.xticks(rotation=0)
plt.title("Age groups/End Time(hour)", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON LEFT, ROW 2:  Age groups vs. Ride Start Time Day
plt.subplot(2,2,3)
sb.boxplot(data=df, x='net_age_groups', y='start_time_day', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xlabel('Age group')
plt.ylabel('Satrt Time(hour)')
plt.xticks(rotation=0)
plt.title("Age groups/Start Time(day)", y=1.05, fontsize=14, fontweight='bold');

# BOX PLOT ON RIGHT, ROW 2:  Age groups vs. Ride End Time Day
plt.subplot(2,2,4)
sb.boxplot(data=df, x='net_age_groups', y='end_time_day', showmeans=True, 
           meanprops={"marker":"o",
                       "markerfacecolor":"white", 
                      "markersize":"10"})
plt.xlabel('Age group')
plt.ylabel('End Time(day)')
plt.xticks(rotation=0)
plt.title("Age groups/End Time(day)", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for Age groups vs. Ride start and End Time Hour and Day
df.groupby('net_age_groups')[['start_time_hour', 'end_time_hour', 'start_time_day', 'end_time_day']].mean()


# plot bar plot for Age groups vs. Bike Id and Ride Distance

# figure size
plt.figure(figsize =[20, 8])

# Bar PLOT ON LEFT, ROW 1:  Age groups vs. Ride Distance
plt.subplot(1,2,1)
sb.barplot(data=df, x='net_age_groups', y='distance_km')
plt.xticks(rotation=0)
plt.xlabel('Age group')
plt.ylabel('Avg. Distance(km)')
plt.title("Avg.Distance(km)'/Age groups - Bar Plot", y=1.05, fontsize=14, fontweight='bold');


# Bar PLOT ON RIGHT, ROW 2:  Age groups vs. Bike Id 
plt.subplot(1, 2, 2)
sb.barplot(data=df, x='net_age_groups', y='bike_id')
plt.xticks(rotation=0)
plt.xlabel('End Weekday')
plt.ylabel('Avg. Range of Bike Ids most used')
plt.title("Avg. Range of Bike Ids/Age groups", y=1.05, fontsize=14, fontweight='bold');


# Check summary statistics for Age groups vs. Bike Id and Ride Distance
df.groupby('net_age_groups')[['distance_km', 'bike_id']].mean()


# Create a correlation matrix of numeric variables
# figure size
plt.figure(figsize =[15, 8])
# Create a dataframe of variables of interest by filtering the original df
corr_vars = df[['duration_sec','duration_minute', 'duration_hour','start_time_hour','end_time_hour','start_time_day',
                'end_time_day', 'member_age', 'distance_km', 'bike_id']]
# Create a heatmap with a diverging color map with annotation of correlation values
sb.heatmap(corr_vars.corr(), annot = True, fmt = '.2f', cmap = 'vlag_r', center = 0)
plt.title("Numeric Variables Correlation Matrix", y=1.05, fontsize=14, fontweight='bold');


# plotting Avg. Durations (second, minute, and hour)/User Types and Member Gender
# x variables are numeric and y varaibles are categorical
g = sb.PairGrid(data = df,  aspect = 2, height = 2, x_vars = ['duration_sec','duration_minute','duration_hour'],
                y_vars = ['user_type','member_gender'])
g.map(sb.barplot)
g.fig.suptitle("Avg. Durations (second, minute, and hour)/User Types and Member Gender", y=1.05, 
               fontsize=14, fontweight='bold');


# Check summary statistics for Durations (second, minute, and hour)
(df.groupby('user_type')[['duration_sec','duration_minute','duration_hour']].mean(), 
df.groupby('member_gender')[['duration_sec','duration_minute','duration_hour']].mean())

(            duration_sec  duration_minute  duration_hour
 user_type                                               
 Customer     1432.465019        23.874411       0.397911
 Subscriber    640.263678        10.671055       0.177846,
                duration_sec  duration_minute  duration_hour
 member_gender                                              
 Female           779.070390        12.984508       0.216419
 Male             672.639069        11.210645       0.186836
 Not_Defined     1189.462311        19.824347       0.330428
 Other            996.515334        16.608535       0.276717)


# plotting Avg. Start and End Time(hour and day)/User Types and Member Gender
# x variables are numeric and y varaibles are categorical
g = sb.PairGrid(data = df,  aspect = 2, height = 2, x_vars = ['start_time_hour','end_time_hour', 'start_time_day',
                'end_time_day'], y_vars = ['user_type','member_gender'])
g.map(sb.barplot)
g.fig.suptitle("Avg. Start and End Time(hour and day)/User Types and Member Gender", y=1.05, 
               fontsize=14, fontweight='bold');


# Check summary statistics for Start and End Time Hours and Days
(df.groupby('user_type')[['start_time_hour','end_time_hour', 'start_time_day',
                'end_time_day']].mean(), 
df.groupby('member_gender')[['start_time_hour','end_time_hour', 'start_time_day',
                'end_time_day']].mean())

(            start_time_hour  end_time_hour  start_time_day  end_time_day
 user_type                                                               
 Customer          13.644806      13.911969       15.718643     15.718039
 Subscriber        13.435779      13.578664       15.247994     15.247285,
                start_time_hour  end_time_hour  start_time_day  end_time_day
 member_gender                                                              
 Female               13.216335      13.387474       15.378244     15.378440
 Male                 13.525277      13.674017       15.291976     15.291341
 Not_Defined          13.503448      13.721355       14.964186     14.961887
 Other                13.672234      13.796002       15.420591     15.411281)


# plotting bar plot for Avg. Member Age, Trip Distance(km), and Bike Ids(most-used)
g = sb.PairGrid(data = df, x_vars = ['member_age', 'distance_km', 'bike_id'], y_vars = ['user_type','member_gender'],
                aspect = 2, height = 2)
g.map(sb.barplot)
g.fig.suptitle("Avg. Member Age, Trip Distance(km), and Bike Ids(most-used)/User Types and Member Gender", y=1.05, 
               fontsize=14, fontweight='bold');


# Check summary statistics for Member Age, Trip Distance(km), and Bike Ids
(df.groupby('user_type')[['member_age', 'distance_km', 'bike_id']].mean(), 
df.groupby('member_gender')[['member_age', 'distance_km', 'bike_id']].mean())

(            member_age  distance_km      bike_id
 user_type                                       
 Customer     28.158295     1.867206  4225.550181
 Subscriber   33.198717     1.669211  4502.956226,
                member_age  distance_km      bike_id
 member_gender                                      
 Female          33.217462     1.765259  4396.909020
 Male            34.451600     1.662637  4507.239707
 Not_Defined      0.000000     1.718845  4274.845856
 Other           35.878970     1.795024  4542.819277)


# plotting Avg. Durations (second, minute, and hour)/Start and End Weekday
# x variables are numeric and y varaibles are categorical
g = sb.PairGrid(data = df,  aspect = 2, height = 2, x_vars = ['duration_sec', 'duration_minute', 'duration_hour'],
                y_vars = ['start_time_weekday', 'end_time_weekday'])
g.map(sb.barplot)
g.fig.suptitle("Avg. Durations (second, minute, and hour)/Start and End Weekday", y=1.05, 
               fontsize=14, fontweight='bold');


# Check summary statistics for Durations (second, minute, and hour)
(df.groupby('start_time_weekday')[['duration_sec', 'duration_minute', 'duration_hour']].mean(), 
df.groupby('end_time_weekday')[['duration_sec', 'duration_minute', 'duration_hour']].mean())

(                    duration_sec  duration_minute  duration_hour
 start_time_weekday                                              
 Friday                695.795073        11.596545       0.193272
 Monday                713.159616        11.886002       0.198095
 Saturday              902.661993        15.044383       0.250749
 Sunday                919.746054        15.329088       0.255455
 Thursday              699.040998        11.650676       0.194160
 Tuesday               663.305567        11.055108       0.184277
 Wednesday             673.671165        11.227834       0.187119,
                   duration_sec  duration_minute  duration_hour
 end_time_weekday                                              
 Friday              702.620027        11.710295       0.195169
 Monday              714.374474        11.906248       0.198433
 Saturday            885.393499        14.756574       0.245953
 Sunday              931.158732        15.519300       0.258625
 Thursday            700.350092        11.672495       0.194524
 Tuesday             667.603771        11.126746       0.185471
 Wednesday           662.669704        11.044475       0.184062)


# plotting Avg. Start and End Time(hour and day)/Start and End Weekday
# x variables are numeric and y varaibles are categorical
g = sb.PairGrid(data = df,  aspect = 1.5, height = 2, x_vars = ['start_time_hour','end_time_hour',
                'start_time_day', 'end_time_day'], y_vars = ['start_time_weekday', 'end_time_weekday'])
g.map(sb.barplot)
g.fig.suptitle("Avg. Start and End Time(hour and day)/Start and End Weekday", y=1.05, 
               fontsize=14, fontweight='bold');


# Check summary statistics for Start and End Time(hour and day)
(df.groupby('start_time_weekday')[['start_time_hour','end_time_hour','start_time_day', 'end_time_day']].mean(), 
df.groupby('end_time_weekday')[['start_time_hour','end_time_hour','start_time_day', 'end_time_day']].mean())

(                    start_time_hour  end_time_hour  start_time_day  \
 start_time_weekday                                                   
 Friday                    12.823022      12.971671       12.725199   
 Monday                    13.395241      13.552585       14.707508   
 Saturday                  13.652126      13.826550       14.300097   
 Sunday                    14.178058      14.360562       14.679894   
 Thursday                  13.620706      13.773731       17.969657   
 Tuesday                   13.229717      13.383711       14.586521   
 Wednesday                 13.712122      13.858473       16.787996   
 
                     end_time_day  
 start_time_weekday                
 Friday                 12.727097  
 Monday                 14.708997  
 Saturday               14.303148  
 Sunday                 14.682922  
 Thursday               17.958491  
 Tuesday                14.587559  
 Wednesday              16.789447  ,
                   start_time_hour  end_time_hour  start_time_day  end_time_day
 end_time_weekday                                                              
 Friday                  12.822056      12.972603       12.734171     12.720610
 Monday                  13.397930      13.549574       14.711568     14.713318
 Saturday                13.658924      13.816583       14.287874     14.291442
 Sunday                  14.176770      14.362237       14.682278     14.685306
 Thursday                13.617223      13.778457       17.971977     17.973199
 Tuesday                 13.231332      13.381270       14.584695     14.585952
 Wednesday               13.710337      13.861766       16.782019     16.783133)


# plotting Avg. Member Age, Trip Distance(km), and Bike Ids/Start and End Weekday
# x variables are numeric and y varaibles are categorical
g = sb.PairGrid(data = df,  aspect = 1.5, height = 2, x_vars = ['member_age','distance_km',
                'bike_id'], y_vars = ['start_time_weekday', 'end_time_weekday'])
g.map(sb.barplot)
g.fig.suptitle("Avg. Member Age, Trip Distance(km), and Bike Ids/Start and End Weekday", y=1.05, 
               fontsize=14, fontweight='bold');


# Check summary statistics for Member Age, Trip Distance(km), and Bike Ids
(df.groupby('start_time_weekday')[['member_age', 'distance_km', 'bike_id']].mean(), 
df.groupby('end_time_weekday')[['member_age', 'distance_km', 'bike_id']].mean())

(                    member_age  distance_km      bike_id
 start_time_weekday                                      
 Friday               32.944757     1.721757  4433.640696
 Monday               32.751527     1.673051  4380.645650
 Saturday             31.065758     1.608491  4628.002207
 Sunday               30.709077     1.608656  4575.100496
 Thursday             33.078359     1.724219  4490.056681
 Tuesday              33.160500     1.712082  4400.652249
 Wednesday            33.069903     1.699010  4517.935697,
                   member_age  distance_km      bike_id
 end_time_weekday                                      
 Friday             32.938511     1.722522  4434.009420
 Monday             32.731040     1.672490  4380.701255
 Saturday           31.067086     1.609141  4627.180432
 Sunday             30.730658     1.608401  4576.550860
 Thursday           33.087139     1.723960  4490.638170
 Tuesday            33.165179     1.712113  4399.857480
 Wednesday          33.067733     1.698878  4517.374439)


# plotting Avg. Durations (second, minute, and hour)/Bike Share Status and Age Groups
# x variables are numeric and y varaibles are categorical
g = sb.PairGrid(data = df,  aspect = 2, height = 2, x_vars = ['duration_sec', 'duration_minute', 'duration_hour'],
                y_vars = ['bike_share_for_all_trip','net_age_groups'])
g.map(sb.barplot)
g.fig.suptitle("Avg. Start and End Time(hour and day)/Bike Share Status and Age Groups ", y=1.05, 
               fontsize=14, fontweight='bold');


# Check summary statistics for Durations (second, minute, and hour)
(df.groupby('bike_share_for_all_trip')[['duration_sec','duration_minute','duration_hour']].mean(), 
df.groupby('net_age_groups')[['duration_sec','duration_minute','duration_hour']].mean())

(                         duration_sec  duration_minute  duration_hour
 bike_share_for_all_trip                                              
 No                         730.454253        12.174229       0.202899
 Yes                        684.220174        11.403686       0.190069,
                 duration_sec  duration_minute  duration_hour
 net_age_groups                                              
 0-20              724.248113        12.070729       0.201191
 20-30             707.930257        11.798826       0.196635
 30-40             687.884419        11.464745       0.191076
 40-50             713.497823        11.891640       0.198202
 50-60             736.022779        12.267001       0.204435
 60-70             764.203723        12.736785       0.212274
 70-140            626.126957        10.435287       0.174052)


# plotting Avg. Start and End Time Hours and Days/Bike Share Status and Age Groups
# x variables are numeric and y varaibles are categorical
g = sb.PairGrid(data = df,  aspect = 2, height = 2, x_vars = ['start_time_hour','end_time_hour',
                'start_time_day', 'end_time_day'],
                y_vars = ['bike_share_for_all_trip','net_age_groups'])
g.map(sb.barplot)
g.fig.suptitle("Avg. Start and End Time(hour and day)/Bike Share Status and Age Groups ", y=1.05, 
               fontsize=14, fontweight='bold');


# Check summary statistics for Start and End Time(hour and day)
(df.groupby('bike_share_for_all_trip')[['start_time_hour','end_time_hour','start_time_day', 'end_time_day']].mean(), 
df.groupby('net_age_groups')[['start_time_hour','end_time_hour','start_time_day', 'end_time_day']].mean())

(                         start_time_hour  end_time_hour  start_time_day  \
 bike_share_for_all_trip                                                   
 No                             13.387587      13.546350       15.298357   
 Yes                            14.136010      14.269255       15.304914   
 
                          end_time_day  
 bike_share_for_all_trip                
 No                          15.297863  
 Yes                         15.302264  ,
                 start_time_hour  end_time_hour  start_time_day  end_time_day
 net_age_groups                                                              
 0-20                  14.376415      14.508962       14.945519     14.948349
 20-30                 13.745989      13.894302       15.409927     15.409057
 30-40                 13.309411      13.462214       15.259697     15.259290
 40-50                 13.104036      13.266984       15.224127     15.222993
 50-60                 13.016952      13.185326       15.258167     15.257108
 60-70                 12.874112      13.059222       15.392893     15.393909
 70-140                12.820870      13.003478       16.744348     16.744348)


# plotting Avg. Member Age, Trip Distance(km), and Bike Ids/Bike Share Status and Age Groups
# x variables are numeric and y varaibles are categorical
g = sb.PairGrid(data = df,  aspect = 2, height = 2, x_vars = ['member_age', 'distance_km', 'bike_id'],
                y_vars = ['bike_share_for_all_trip','net_age_groups'])
g.map(sb.barplot)
g.fig.suptitle("Avg. Start and End Time(hour and day)/Bike Share Status and Age Groups ", y=1.05, 
               fontsize=14, fontweight='bold');


# Check summary statistics for Member Age, Trip Distance(km), and Bike Ids
(df.groupby('bike_share_for_all_trip')[['member_age', 'distance_km', 'bike_id']].mean(), 
df.groupby('net_age_groups')[['member_age', 'distance_km', 'bike_id']].mean())

(                         member_age  distance_km      bike_id
 bike_share_for_all_trip                                      
 No                        32.669834     1.731022  4482.769845
 Yes                       32.488968     1.304558  4378.554237,
                 member_age  distance_km      bike_id
 net_age_groups                                      
 0-20             19.588208     1.299768  4403.791509
 20-30            26.295486     1.654897  4517.431520
 30-40            34.684954     1.767911  4520.030210
 40-50            44.919184     1.687907  4409.832245
 50-60            54.708017     1.642379  4331.067544
 60-70            63.942809     1.601229  4090.001015
 70-140           82.109565     1.461706  4344.766957)


# plot scatter plot for Member Age and Ride Distance(km) vs. Ride Start and End Times in Hours and Days

# figure size
plt.figure(figsize =[20, 15])

# SCATTER PLOT ON LEFT, ROW 1:  Member Age and Distance(km)/Start Time(hour)
plt.subplot(2,2,1)
# plot scatter plot
plt.scatter(data = df, x = 'member_age', y = 'distance_km', c = 'start_time_hour',
            s = 'start_time_hour', cmap = 'viridis_r')
plt.colorbar()
# dummy series for adding legend
sizes = [100, 200, 300]
base_color = sb.color_palette()[0]
legend_obj = []
for s in sizes:
    legend_obj.append(plt.scatter([], [], s = s, color = base_color))
plt.legend(legend_obj, sizes, title = 'Start Time(hour)')
# labels and title
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Member Age and Distance(km)/Start Time(hour)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON LEFT, ROW 1:  Member Age and Distance(km)/End Time(hour)
plt.subplot(2,2,2)
# plot scatter plot
plt.scatter(data = df, x = 'member_age', y = 'distance_km', c = 'start_time_hour',
            s = 'end_time_hour', cmap = 'viridis_r')
plt.colorbar()
# dummy series for adding legend
sizes = [100, 200, 300]
base_color = sb.color_palette()[0]
legend_obj = []
for s in sizes:
    legend_obj.append(plt.scatter([], [], s = s, color = base_color))
plt.legend(legend_obj, sizes, title = 'Start Time(hour)')
# labels and title
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Member Age and Distance(km)/End Time(hour)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON LEFT, ROW 2:  Member Age and Distance(km)/Start Time(day)
plt.subplot(2,2,3)
# plot scatter plot
plt.scatter(data = df, x = 'member_age', y = 'distance_km', c = 'start_time_day', 
            s = 'start_time_day', cmap = 'viridis_r')
plt.colorbar()
# dummy series for adding legend
sizes = [100, 200, 300]
base_color = sb.color_palette()[0]
legend_obj = []
for s in sizes:
    legend_obj.append(plt.scatter([], [], s = s, color = base_color))
plt.legend(legend_obj, sizes, title = 'Start Time(day)')
# labels and title
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Member Age and Distance(km)/Start Time(day)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON RIGHT, ROW 2:  Member Age and Distance(km)/End Time(day)
plt.subplot(2,2,4)
# plot scatter plot
plt.scatter(data = df, x = 'member_age', y = 'distance_km', c = 'start_time_day', 
            s = 'end_time_day', cmap = 'viridis_r')
plt.colorbar()
# dummy series for adding legend
sizes = [100, 200, 300]
base_color = sb.color_palette()[0]
legend_obj = []
for s in sizes:
    legend_obj.append(plt.scatter([], [], s = s, color = base_color))
plt.legend(legend_obj, sizes, title = 'Start Time(day)')
# labels and title
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Member Age and Distance(km)/End Time(day)", y=1.05, fontsize=14, fontweight='bold');


# plot scatter plot for Member Age and Ride Distance(km) vs. Durations in Seconds, Minutes, and Hours

# figure size
plt.figure(figsize =[20, 15])

# SCATTER PLOT ON LEFT, ROW 1:  Member Age and Distance(km)/Duration(second)
plt.subplot(3, 1, 1)
# plot scatter plot
plt.scatter(data = df, x = 'member_age', y = 'distance_km', c = 'duration_sec',
            s = 'duration_sec', cmap = 'viridis_r')
plt.colorbar()
# dummy series for adding legend
sizes = [200, 350, 500]
base_color = sb.color_palette()[0]
legend_obj = []
for s in sizes:
    legend_obj.append(plt.scatter([], [], s = s, color = base_color))
plt.legend(legend_obj, sizes, title = 'Duration(second)')
# labels and title
plt.xticks(rotation=0)
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Duration(second)/Member Age and Distance(km)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON MIDDLE, ROW 1:  Member Age and Distance(km)/Duration(minute)
plt.subplot(3, 1, 2)
# plot scatter plot
plt.scatter(data = df, x = 'member_age', y = 'distance_km', c = 'duration_minute',
            s = 'duration_minute', cmap ='viridis_r')
plt.colorbar()
# dummy series for adding legend
sizes = [100, 200, 300]
base_color = sb.color_palette()[0]
legend_obj = []
for s in sizes:
    legend_obj.append(plt.scatter([], [], s = s, color = base_color))
plt.legend(legend_obj, sizes, title = 'Duration(minute)')
# labels and title
plt.xticks(rotation=0)
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Duration(minute)/Member Age and Distance(km)", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON RIGHT, ROW 1:  Member Age and Distance(km)/Duration(hour)
plt.subplot(3, 1, 3)
# plot scatter plot
plt.scatter(data = df, x = 'member_age', y = 'distance_km', c = 'duration_hour',
            s = 'duration_hour', cmap = 'viridis_r')
plt.colorbar()
# dummy series for adding legend
sizes = [100, 200, 300]
base_color = sb.color_palette()[0]
legend_obj = []
for s in sizes:
    legend_obj.append(plt.scatter([], [], s = s, color = base_color))
plt.legend(legend_obj, sizes, title = 'Duration(hour)')
# labels and title
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Duration(hour)/Member Age and Distance(km)", y=1.05, fontsize=14, fontweight='bold');


# plot scatter plot for Member Age and Ride Distance(km) vs. Bike Id

# figure size
plt.figure(figsize =[20, 5])
# plot scatter plot
plt.scatter(data = df, x = 'member_age', y = 'distance_km', c = 'bike_id', s = 'bike_id', 
            cmap = 'viridis_r')
plt.colorbar()
# dummy series for adding legend
sizes = [200, 350, 500]
base_color = sb.color_palette()[0]
legend_obj = []
for s in sizes:
    legend_obj.append(plt.scatter([], [], s = s, color = base_color))
plt.legend(legend_obj, sizes, title = 'Bike Ids Most-used')
# labels and title
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Bike Ids Most-used/Member Age and Distance(km)", y=1.05, fontsize=14, fontweight='bold');


# plot scatter plot for Member Age and Ride Distance(km) vs. User Type

# hue = the categorical variable that the other 2 numeric variables in the map() funtion are compared against it
grid = sb.FacetGrid(data = df, hue = 'user_type', hue_order = ['Subscriber', 'Customer'], height = 5, 
                    aspect= 2.5, palette = 'viridis')
grid.map(sb.regplot, 'member_age', 'distance_km', x_jitter = 0.04, fit_reg = False)
grid.add_legend(title ='User Types')
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Member Age and Distance(km)/User Type", y=1.05, fontsize=14, fontweight='bold');


# plot scatter plot for Member Age and Ride Distance(km) vs. Member Gender
# hue = the categorical variable that the other 2 numeric variables in the map() funtion are compared against it
grid = sb.FacetGrid(data = df, hue = 'member_gender', hue_order = ['Male', 'Female', 'Other'], height = 5, 
                    aspect= 2.5,  palette = 'viridis_r')
grid.map(sb.regplot, 'member_age', 'distance_km', x_jitter = 0.04, fit_reg = False)
grid.add_legend(title ='Member Genders')
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Member Age and Distance(km)/Member Gender", y=1.05, fontsize=14, fontweight='bold');


# plot scatter plot for Member Age and Ride Distance(km) vs. Age Groups
# hue = the categorical variable that the other 2 numeric variables in the map() funtion are compared against it
grid = sb.FacetGrid(data = df, hue = 'net_age_groups', height = 5, 
                    aspect= 2.5,  palette = 'viridis_r')
grid.map(sb.regplot, 'member_age', 'distance_km', x_jitter = 0.04, fit_reg = False)
grid.add_legend(title ='Age Group')
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Member Age and Distance(km)/Age Group", y=1.05, fontsize=14, fontweight='bold');


# plot scatter plot for Member Age and Ride Distance(km) vs. Ride Start and End Weekdays
# hue = the categorical variable that the other 2 numeric variables in the map() funtion are compared against it

# plot scatter plot: Member Age and Ride Distance(km) vs. Ride Start Weekdays 
grid = sb.FacetGrid(data = df, hue = 'start_time_weekday', height = 5,  aspect = 2.5, 
                    palette = 'viridis_r')
grid.map(sb.regplot, 'member_age', 'distance_km', x_jitter = 0.04, fit_reg = False)
grid.add_legend(title ='Weekday')
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Member Age and Distance(km)/Start Time Weekday", y=1.05, fontsize=14, fontweight='bold');


# plot scatter plot: Member Age and Ride Distance(km) vs. Ride End Weekdays 
grid = sb.FacetGrid(data = df, hue = 'end_time_weekday', height = 5, aspect = 2.5, 
                    palette = 'viridis_r')
grid.map(sb.regplot, 'member_age', 'distance_km', x_jitter = 0.04, fit_reg = False)
grid.add_legend(title ='Weekday')
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Member Age and Distance(km)/End Time Weekday", y=1.05, fontsize=14, fontweight='bold');


# plot scatter plot for Member Age and Distance(km)/Bike Share Status
# hue = the categorical variable that the other 2 numeric variables in the map() funtion are compared against it

grid = sb.FacetGrid(data = df, hue = 'bike_share_for_all_trip', height = 5,  aspect = 2.5, 
                 palette = 'viridis_r')
grid.map(sb.regplot, 'member_age', 'distance_km', x_jitter = 0.04, fit_reg = False)
grid.add_legend(title ='Bike Share Status')
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.xticks(rotation=0)
plt.title("Member Age and Distance(km)/Bike Share Status", y=1.05, fontsize=14, fontweight='bold');


# plot scatter plot for Member Age and Ride Distance(km) vs. User Type
# Figure size
plt.figure(figsize = [20, 5])

# SCATTER PLOT ON LEFT, ROW 1:  San Fransisco Member Age and Ride Distance(km) vs. User Type
plt.subplot(1, 3, 1)
ttype_markers = [['Subscriber', '^'],
               ['Customer', 'o']]

for ttype, marker in ttype_markers:
    plot_data = San_Fransisco[San_Fransisco['user_type'] == ttype]
    plt.scatter(data = San_Fransisco, x = 'member_age', y = 'distance_km', marker = marker)
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.legend(['Customer','Subscriber'])
plt.title("San Fransisco User Type", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON MIDDLE, ROW 1:  Oakland_Berkeley Member Age and Ride Distance(km) vs. User Type
plt.subplot(1, 3, 2)
ttype_markers = [['Subscriber', '^'],
               ['Customer', 'o']]

for ttype, marker in ttype_markers:
    plot_data = Oakland_Berkeley[Oakland_Berkeley['user_type'] == ttype]
    plt.scatter(data = Oakland_Berkeley, x = 'member_age', y = 'distance_km', marker = marker)
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.legend(['Customer','Subscriber'])
plt.title("Oakland-Berkeley User Type", y=1.05, fontsize=14, fontweight='bold');



# SCATTER PLOT ON RIGHT, ROW 1:  San Jose Member Age and Ride Distance(km) vs. User Type
plt.subplot(1, 3, 3)
ttype_markers = [['Subscriber', '^'],
               ['Customer', 'o']]

for ttype, marker in ttype_markers:
    plot_data = San_Jose[San_Jose['user_type'] == ttype]
    plt.scatter(data = San_Jose, x = 'member_age', y = 'distance_km', marker = marker)
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.legend(['Customer','Subscriber'])
plt.title("San Jose User Type", y=1.05, fontsize=14, fontweight='bold');


# plot scatter plot for Member Age and Ride Distance(km) vs. Member Gender
# Figure size
plt.figure(figsize = [20, 5])

# SCATTER PLOT ON LEFT, ROW 1:  San Fransisco Member Age and Ride Distance(km) vs. User Type
plt.subplot(1, 3, 1)
ttype_markers = [['Male', 'v'],
               ['Female', 'o'],
               ['Other', 'x'],
               ['Not-defined', '^']]

for ttype, marker in ttype_markers:
    plot_data = San_Fransisco[San_Fransisco['member_gender'] == ttype]
    plt.scatter(data = San_Fransisco, x = 'member_age', y = 'distance_km', marker = marker)
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.legend(['Male','Female', 'Other', 'Not-defined'])
plt.title("San Fransisco Member Gender", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON MIDDLE, ROW 1:  Oakland_Berkeley Member Age and Ride Distance(km) vs. User Type
plt.subplot(1, 3, 2)
ttype_markers = [['Male', 'v'],
               ['Female', 'o'],
               ['Other', 'x'],
               ['Not-defined', '^']]

for ttype, marker in ttype_markers:
    plot_data = Oakland_Berkeley[Oakland_Berkeley['member_gender'] == ttype]
    plt.scatter(data = Oakland_Berkeley, x = 'member_age', y = 'distance_km', marker = marker)
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.legend(['Male','Female', 'Other', 'Not-defined'])
plt.title("Oakland Berkeley Member Gender", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON RIGHT, ROW 1:  San Jose Member Age and Ride Distance(km) vs. User Type
plt.subplot(1, 3, 3)
ttype_markers = [['Male', 'v'],
               ['Female', 'o'],
               ['Other', 'x'],
               ['Not-defined', '^']]

for ttype, marker in ttype_markers:
    plot_data = San_Jose[San_Jose['member_gender'] == ttype]
    plt.scatter(data = San_Jose, x = 'member_age', y = 'distance_km', marker = marker)
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.legend(['Male','Female', 'Other', 'Not-defined'])
plt.title("San Jose Member Gender", y=1.05, fontsize=14, fontweight='bold');


# plot scatter plot for Member Age and Ride Distance(km) vs. Bike Share Status
# Figure size
plt.figure(figsize = [20, 5])

# SCATTER PLOT ON LEFT, ROW 1:  San Fransisco Member Age and Ride Distance(km) vs. Bike Share Status
plt.subplot(1, 3, 1)
ttype_markers = [['Yes', '^'],
               ['No', 'o']]

for ttype, marker in ttype_markers:
    plot_data = San_Fransisco[San_Fransisco['bike_share_for_all_trip'] == ttype]
    plt.scatter(data = San_Fransisco, x = 'member_age', y = 'distance_km', marker = marker)
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.legend(['Yes','No'])
plt.title("San Fransisco Bike Share Status", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON MIDDLE, ROW 1:  Oakland_Berkeley Member Age and Ride Distance(km) vs. Bike Share Status
plt.subplot(1, 3, 2)
ttype_markers = [['Yes', '^'],
               ['No', 'o']]

for ttype, marker in ttype_markers:
    plot_data = Oakland_Berkeley[Oakland_Berkeley['bike_share_for_all_trip'] == ttype]
    plt.scatter(data = Oakland_Berkeley, x = 'member_age', y = 'distance_km', marker = marker)
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.legend(['Yes','No'])
plt.title("Oakland Berkeley Bike Share Status", y=1.05, fontsize=14, fontweight='bold');



# SCATTER PLOT ON RIGHT, ROW 1:  San Jose Member Age and Ride Distance(km) vs. Bike Share Status
plt.subplot(1, 3, 3)
ttype_markers = [['Yes', '^'],
               ['No', 'o']]

for ttype, marker in ttype_markers:
    plot_data = San_Jose[San_Jose['bike_share_for_all_trip'] == ttype]
    plt.scatter(data = San_Jose, x = 'member_age', y = 'distance_km', marker = marker)
plt.xlabel('Member Age')
plt.ylabel('Distance(km)')
plt.legend(['Yes','No'])
plt.title("San Jose Bike Share Status", y=1.05, fontsize=14, fontweight='bold');


# plot scatter plot Member Age and Duration vs. Ride Weekdays
# Figure size
plt.figure(figsize = [20, 5])

# SCATTER PLOT ON LEFT, ROW 1:  San Fransisco Member Age and Ride Distance(km) vs. Ride Weekdays
plt.subplot(1, 3, 1)
ttype_markers = [['Saturday', 'o'],
               ['Sunday', 'v'],
               ['Monday', '^'],
               ['Tuesday', 'h'],
               ['Wednesday', 'p'],
               ['Thursday', 's'],
               ['Friday', 'H']]

for ttype, marker in ttype_markers:
    plot_data = df[df['start_time_day'] == ttype]
    plt.scatter(data = San_Fransisco, x = 'member_age', y = 'duration_hour', marker = marker)
plt.xlabel('Member Age')
plt.ylabel('Duration(hour)')
plt.title("San Fransisco Weekday", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON MIDDLE, ROW 1:  Oakland_Berkeley Member Age and Ride Distance(km) vs. Ride Weekdays
plt.subplot(1, 3, 2)
ttype_markers = [['Saturday', 'o'],
               ['Sunday', 'v'],
               ['Monday', '^'],
               ['Tuesday', 'h'],
               ['Wednesday', 'H'],
               ['Thursday', 's'],
               ['Friday', 'p']]

for ttype, marker in ttype_markers:
    plot_data = df[df['start_time_day'] == ttype]
    plt.scatter(data = Oakland_Berkeley, x = 'member_age', y = 'duration_hour', marker = marker)
plt.xlabel('Member Age')
plt.ylabel('Duration(hour)')
plt.title("Oakland Berkeley Weekday", y=1.05, fontsize=14, fontweight='bold');



# SCATTER PLOT ON RIGHT, ROW 1:  San Jose Member Age and Ride Distance(km) vs. Ride Weekdays
plt.subplot(1, 3, 3)
ttype_markers = [['Saturday', 'o'],
               ['Sunday', 'v'],
               ['Monday', '^'],
               ['Tuesday', 'h'],
               ['Wednesday', 'H'],
               ['Thursday', 's'],
               ['Friday', 'p']]

for ttype, marker in ttype_markers:
    plot_data = df[df['start_time_day'] == ttype]
    plt.scatter(data = San_Jose, x = 'member_age', y = 'duration_hour', marker = marker)
plt.xlabel('Member Age')
plt.ylabel('Duration(hour)')
plt.title("San Jose Weekday", y=1.05, fontsize=14, fontweight='bold');


# plot scatter plot Member Age and Duration vs. Ride Weekdays
# Figure size
plt.figure(figsize = [20, 5])

# SCATTER PLOT ON LEFT, ROW 1:  San Fransisco Member Age and Weekdays vs. Start Time Hour
plt.subplot(1, 3, 1)
ttype_markers = [['Saturday', 'o'],
               ['Sunday', 'v'],
               ['Monday', '^'],
               ['Tuesday', 'h'],
               ['Wednesday', 'p'],
               ['Thursday', 's'],
               ['Friday', 'H']]

for ttype, marker in ttype_markers:
    plot_data = df[df['start_time_day'] == ttype]
    plt.scatter(data = San_Fransisco, x = 'member_age', y = 'start_time_hour', marker = marker)
plt.xlabel('Member Age')
plt.ylabel('Start Time(hour)')
plt.title("San Fransisco Weekday", y=1.05, fontsize=14, fontweight='bold');

# SCATTER PLOT ON MIDDLE, ROW 1:  Oakland_Berkeley Member Age and Weekdays vs. Start Time Hour
plt.subplot(1, 3, 2)
ttype_markers = [['Saturday', 'o'],
               ['Sunday', 'v'],
               ['Monday', '^'],
               ['Tuesday', 'h'],
               ['Wednesday', 'H'],
               ['Thursday', 's'],
               ['Friday', 'p']]

for ttype, marker in ttype_markers:
    plot_data = df[df['start_time_day'] == ttype]
    plt.scatter(data = Oakland_Berkeley, x = 'member_age', y = 'start_time_hour', marker = marker)
plt.xlabel('Member Age')
plt.ylabel('Start Time(hour)')
plt.title("Oakland Berkeley Weekday", y=1.05, fontsize=14, fontweight='bold');



# SCATTER PLOT ON RIGHT, ROW 1:  San Jose Member Age and Weekdays vs. Start Time Hour
plt.subplot(1, 3, 3)
ttype_markers = [['Saturday', 'o'],
               ['Sunday', 'v'],
               ['Monday', '^'],
               ['Tuesday', 'h'],
               ['Wednesday', 'H'],
               ['Thursday', 's'],
               ['Friday', 'p']]

for ttype, marker in ttype_markers:
    plot_data = df[df['start_time_day'] == ttype]
    plt.scatter(data = San_Jose, x = 'member_age', y = 'start_time_hour', marker = marker)
plt.xlabel('Member Age')
plt.ylabel('Start Time(hour)')
plt.title("San Jose Weekday", y=1.05, fontsize=14, fontweight='bold');


# plot Heat Map for User Type and Member Gender vs. Member Age
plt.figure(figsize =[20, 8])

# filter categorical variables by numeric variable
cat_means = ages_minus_0.groupby(['user_type','member_gender']).mean()['member_age']
# index and order variables
cat_means = cat_means.reset_index(name = 'distance_km')
cat_means = cat_means.pivot(index = 'member_gender', columns = 'user_type', values = 'distance_km')
# plot heat manp
sb.heatmap(cat_means, annot = True, fmt = '.0f', cmap='rocket_r', cbar_kws = {'label':'Member Age(year)'})
plt.xlabel('User Types')
plt.ylabel('Member Genders')
plt.xticks(rotation=0)
plt.title("Avg. Member Age(year))/ User Type and Memeber Gender", y=1.05, fontsize=14, fontweight='bold');


# plot Heat Map for User Type and Member Gender vs. Distance(km)
plt.figure(figsize =[20, 8])

# filter categorical variables by numeric variable
cat_means = df.groupby(['user_type', 'member_gender']).mean()['distance_km']
# index and order variables
cat_means = cat_means.reset_index(name = 'distance_km')
cat_means = cat_means.pivot(index = 'member_gender', columns = 'user_type', values = 'distance_km')
# plot heat manp
sb.heatmap(cat_means, annot = True, fmt = '.1f', cmap='rocket_r', cbar_kws = {'label':'Avg. Distance(km)'})
plt.xlabel('User Types')
plt.ylabel('Member Genders')
plt.xticks(rotation=0)
plt.title("Avg. Distance(km))/ User Type and Memeber Gender", y=1.05, fontsize=14, fontweight='bold');


# plot Heat Map for User Type and Member Gender vs. Durations in Seconds, Minutes, and Hours

# figure size
plt.figure(figsize =[20, 8])

# HEAT MAP ON LEFT, ROW 1:  Avg. Duration(second)/User Type and Memeber Gender
plt.subplot(1,3,1)

# filter categorical variables by numeric variable
cat_means = df.groupby(['user_type', 'member_gender']).mean()['duration_sec']
# index and order variables
cat_means = cat_means.reset_index(name = 'duration_sec')
cat_means = cat_means.pivot(index = 'member_gender', columns = 'user_type', values = 'duration_sec')
# plot heat manp
sb.heatmap(cat_means, annot = True, fmt = '.1f', cmap='rocket_r', cbar_kws = {'label':'Duration(second)'})
plt.xlabel('User Types')
plt.ylabel('Member Genders')
plt.xticks(rotation=0)
plt.title("Avg. Duration(second)/User and Gender", y=1.05, fontsize=14, fontweight='bold');

# HEAT MAP ON MIDDLE, ROW 1:  Avg. Duration(minute)/User Type and Memeber Gender
plt.subplot(1,3,2)

# filter categorical variables by numeric variable
cat_means = df.groupby(['user_type', 'member_gender']).mean()['duration_minute']
# index and order variables
cat_means = cat_means.reset_index(name = 'duration_minute')
cat_means = cat_means.pivot(index = 'member_gender', columns = 'user_type', values = 'duration_minute')
# plot heat manp
sb.heatmap(cat_means, annot = True, fmt = '.1f', cmap='rocket_r', cbar_kws = {'label':'Duration(minute)'})
plt.xlabel('User Types')
plt.ylabel('Member Genders')
plt.xticks(rotation=0)
plt.title("Avg. Duration(minute)/User and Gender", y=1.05, fontsize=14, fontweight='bold');


# HEAT MAP ON RIGHT, ROW 1:  Avg. Duration(hour)/User Type and Memeber Gender
plt.subplot(1,3,3)
# filter categorical variables by numeric variable
cat_means = df.groupby(['user_type', 'member_gender']).mean()['duration_hour']
# index and order variables
cat_means = cat_means.reset_index(name = 'duration_hour')
cat_means = cat_means.pivot(index = 'member_gender', columns = 'user_type', values = 'duration_hour')
# plot heat manp
sb.heatmap(cat_means, annot = True, fmt = '.1f', cmap='rocket_r', cbar_kws = {'label':'Duration(hour)'})
plt.xlabel('User Types')
plt.ylabel('Member Genders')
plt.xticks(rotation=0)
plt.title("Avg. Duration(hour)/User an Gender", y=1.05, fontsize=14, fontweight='bold');


# plotting 2 categorical variables vs. 1 numeric variable in terms of mean statistic
# plot Heat Map for Avg. Start Time(hour)/User Type and Memeber Gender and
# Avg. Start Time(day)/User Type and Memeber Gender

# figure size
plt.figure(figsize =[20, 8])

# HEAT MAP ON LEFT, ROW 1:  Avg. Start Time(hour)/User Type and Memeber Gender
plt.subplot(1,2,1)
# filter categorical variables by numeric variable
cat_means = df.groupby(['user_type', 'member_gender']).mean()['start_time_hour']
# index and order variables
cat_means = cat_means.reset_index(name = 'start_time_hour')
cat_means = cat_means.pivot(index = 'member_gender', columns = 'user_type', values = 'start_time_hour')
# plot heat manp
sb.heatmap(cat_means, annot = True, fmt = '.0f', cmap='rocket_r', cbar_kws = {'label':'Avg. Start Time(hour)'})
plt.xticks(rotation=0)
plt.xlabel('User Types')
plt.ylabel('Member Genders')
plt.xticks(rotation=0)
plt.title("Avg. Start Time(hour)/ User Type and Memeber Gender", y=1.05, fontsize=14, fontweight='bold');

# HEAT MAP ON Right, ROW 1: Avg. Start Time(day)/User Type and Memeber Gender
plt.subplot(1,2,2)
# filter categorical variables by numeric variable
cat_means = df.groupby(['user_type', 'member_gender']).mean()['start_time_day']
# index and order variables
cat_means = cat_means.reset_index(name = 'start_time_day')
cat_means = cat_means.pivot(index = 'member_gender', columns = 'user_type', values = 'start_time_day')
# plot heat manp
sb.heatmap(cat_means, annot = True, fmt = '.0f', cmap='rocket_r', cbar_kws = {'label':'Avg. Start Time(day)'})
plt.xticks(rotation=0)
plt.xlabel('User Types')
plt.ylabel('Member Genders')
plt.xticks(rotation=0)
plt.title("Avg. Start Time(day)/ User Type and Memeber Gender", y=1.05, fontsize=14, fontweight='bold');


# plot Heat Map for User Type and Member Gender vs. Bike Id
plt.figure(figsize =[20, 8])

# filter categorical variables by numeric variable
cat_means = df.groupby(['user_type', 'member_gender']).mean()['bike_id']
# index and order variables
cat_means = cat_means.reset_index(name = 'bike_id')
cat_means = cat_means.pivot(index = 'member_gender', columns = 'user_type', values = 'bike_id')
# plot heat manp
sb.heatmap(cat_means, annot = True, fmt = '.0f', cmap='rocket_r', cbar_kws = {'label':'Avg. Bike Id'})
plt.xlabel('User Types')
plt.ylabel('Member Genders')
plt.xticks(rotation=0)
plt.title("Avg. Bike Id/ User Type and Memeber Gender", y=1.05, fontsize=14, fontweight='bold');


# plot point plot for User Type and Member Gender vs. numeric variables: 
# Member Age, Durations in Seconds, Minutes, and Hours, Ride Start and End Times in Hour and Day,
# Ride Distance(km), and Bike Id

# figure size
plt.figure(figsize = [ 20,20])

# POINT PLOT ON LEFT, ROW 1: Avg. Age/User Type and Memeber Gender
plt.subplot(4,4,1)
ax = sb.pointplot(data = df, x = 'user_type', y = 'member_age', hue = 'member_gender',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Age", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON LEFT, ROW 1: Avg. Durations(second)/User Type and Memeber Gender
plt.subplot(4,4,2)
ax = sb.pointplot(data = df, x = 'user_type', y = 'duration_sec', hue = 'member_gender',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Duration(second)", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON RIGHT, ROW 1: Avg. Durations(minute)/User Type and Memeber Gender
plt.subplot(4,4,3)
ax = sb.pointplot(data = df, x = 'user_type', y = 'duration_minute', hue = 'member_gender',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Duration(minute)", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON RIGHT, ROW 1: Avg. Durations(hour)/User Type and Memeber Gender
plt.subplot(4,4,4)
ax = sb.pointplot(data = df, x = 'user_type', y = 'duration_hour', hue = 'member_gender',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Duration(hour)", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON RIGHT, ROW 1: Avg. Start Time(hour)/User Type and Memeber Gender
plt.subplot(4,4,5)
ax = sb.pointplot(data = df, x = 'user_type', y = 'start_time_hour', hue = 'member_gender',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Start Time(hour)", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON RIGHT, ROW 1: Avg. Start Time(day)/User Type and Memeber Gender
plt.subplot(4,4,6)
ax = sb.pointplot(data = df, x = 'user_type', y = 'start_time_day', hue = 'member_gender',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Start Time(day)", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON RIGHT, ROW 1: Avg. Distance(km)/User Type and Memeber Gender
plt.subplot(4,4,7)
ax = sb.pointplot(data = df, x = 'user_type', y = 'distance_km', hue = 'member_gender',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Distance(km)", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON RIGHT, ROW 1: Avg. Bike Id/User Type and Memeber Gender
plt.subplot(4,4,8)
ax = sb.pointplot(data = df, x = 'user_type', y = 'bike_id', hue = 'member_gender',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Bike Id", y=1.05, fontsize=14, fontweight='bold');


# plot point plot for User Type and Satart Time Weekday vs. numeric variables:
# Member Age, Durations in Seconds, Minutes, and Hours,
# Ride Start and End Times in Hour and Day, Ride Distance(km), and Bike Id

# figure size
plt.figure(figsize = [ 20,20])

# POINT PLOT ON LEFT, ROW 1: Avg. Age/User Type and Start Time Weekday 
plt.subplot(4,4,1)
ax = sb.pointplot(data = df, x = 'user_type', y = 'member_age', hue = 'start_time_weekday',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Age", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON LEFT, ROW 1: Avg. Durations(second)/User Type and Start Time Weekday 
plt.subplot(4,4,2)
ax = sb.pointplot(data = df, x = 'user_type', y = 'duration_sec', hue = 'start_time_weekday',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Duration(second)", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON RIGHT, ROW 1: Avg. Durations(minute)/User Type and Start Time Weekday 
plt.subplot(4,4,3)
ax = sb.pointplot(data = df, x = 'user_type', y = 'duration_minute', hue = 'start_time_weekday',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Duration(minute)", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON RIGHT, ROW 1: Avg. Durations(hour)/User Type and Start Time Weekday 
plt.subplot(4,4,4)
ax = sb.pointplot(data = df, x = 'user_type', y = 'duration_hour', hue = 'start_time_weekday',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Duration(hour)", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON RIGHT, ROW 1: Avg. Start Time(hour)/User Type and Start Time Weekday 
plt.subplot(4,4,5)
ax = sb.pointplot(data = df, x = 'user_type', y = 'start_time_hour', hue = 'start_time_weekday',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Start Time(hour)", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON RIGHT, ROW 1: Avg. Start Time(day)/User Type and Start Time Weekday 
plt.subplot(4,4,6)
ax = sb.pointplot(data = df, x = 'user_type', y = 'start_time_day', hue = 'start_time_weekday',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Start Time(day)", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON RIGHT, ROW 1: Avg. Distance(km)/User Type and Start Time Weekday 
plt.subplot(4,4,7)
ax = sb.pointplot(data = df, x = 'user_type', y = 'distance_km', hue = 'start_time_weekday',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Distance(km)", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON RIGHT, ROW 1: Avg. Bike Id/User Type and Start Time Weekday 
plt.subplot(4,4,8)
ax = sb.pointplot(data = df, x = 'user_type', y = 'bike_id', hue = 'start_time_weekday',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Bike Id", y=1.05, fontsize=14, fontweight='bold');


# plot point plot for User Type and Bike Share Status vs. numeric variables:
# Member Age, Durations in Seconds, Minutes, and Hours,
# Ride Start and End Times in Hour and Day, Ride Distance(km), and Bike Id

# figure size
plt.figure(figsize = [ 20,20])

# POINT PLOT ON LEFT, ROW 1: Avg. Age/User Type and Bike Share Statusy 
plt.subplot(4,4,1)
ax = sb.pointplot(data = df, x = 'user_type', y = 'member_age', hue = 'bike_share_for_all_trip',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Age", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON LEFT, ROW 1: Avg. Durations(second)/User Type and Bike Share Status 
plt.subplot(4,4,2)
ax = sb.pointplot(data = df, x = 'user_type', y = 'duration_sec', hue = 'bike_share_for_all_trip',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Duration(second)", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON RIGHT, ROW 1: Avg. Durations(minute)/User Type and Bike Share Status
plt.subplot(4,4,3)
ax = sb.pointplot(data = df, x = 'user_type', y = 'duration_minute', hue = 'bike_share_for_all_trip',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Duration(minute)", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON RIGHT, ROW 1: Avg. Durations(hour)/User Type and Bike Share Status
plt.subplot(4,4,4)
ax = sb.pointplot(data = df, x = 'user_type', y = 'duration_hour', hue = 'bike_share_for_all_trip',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Duration(hour)", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON RIGHT, ROW 1: Avg. Start Time(hour)/User Type and Bike Share Status
plt.subplot(4,4,5)
ax = sb.pointplot(data = df, x = 'user_type', y = 'start_time_hour', hue = 'bike_share_for_all_trip',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Start Time(hour)", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON RIGHT, ROW 1: Avg. Start Time(day)/User Type and Bike Share Status
plt.subplot(4,4,6)
ax = sb.pointplot(data = df, x = 'user_type', y = 'start_time_day', hue = 'bike_share_for_all_trip',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Start Time(day)", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON RIGHT, ROW 1: Avg. Distance(km)/User Type and Bike Share Status
plt.subplot(4,4,7)
ax = sb.pointplot(data = df, x = 'user_type', y = 'distance_km', hue = 'bike_share_for_all_trip',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Distance(km)", y=1.05, fontsize=14, fontweight='bold');

# POINT PLOT ON RIGHT, ROW 1: Avg. Bike Id/User Type and Bike Share Status
plt.subplot(4,4,8)
ax = sb.pointplot(data = df, x = 'user_type', y = 'bike_id', hue = 'bike_share_for_all_trip',
                  dodge = 0.3, linestyles = "")
plt.title("Avg. Bike Id", y=1.05, fontsize=14, fontweight='bold');

	duration_sec	start_time	end_time	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	member_birth_year	member_gender	bike_share_for_all_trip
0	52185	2019-02-28 17:32:10.1450	2019-03-01 08:01:55.9750	21.0	Montgomery St BART Station (Market St at 2nd St)	37.789625	-122.400811	13.0	Commercial St at Montgomery St	37.794231	-122.402923	4902	Customer	1984.0	Male	No
1	42521	2019-02-28 18:53:21.7890	2019-03-01 06:42:03.0560	23.0	The Embarcadero at Steuart St	37.791464	-122.391034	81.0	Berry St at 4th St	37.775880	-122.393170	2535	Customer	NaN	NaN	No
2	61854	2019-02-28 12:13:13.2180	2019-03-01 05:24:08.1460	86.0	Market St at Dolores St	37.769305	-122.426826	3.0	Powell St BART Station (Market St at 4th St)	37.786375	-122.404904	5905	Customer	1972.0	Male	No
3	36490	2019-02-28 17:54:26.0100	2019-03-01 04:02:36.8420	375.0	Grove St at Masonic Ave	37.774836	-122.446546	70.0	Central Ave at Fell St	37.773311	-122.444293	6638	Subscriber	1989.0	Other	No
4	1585	2019-02-28 23:54:18.5490	2019-03-01 00:20:44.0740	7.0	Frank H Ogawa Plaza	37.804562	-122.271738	222.0	10th Ave at E 15th St	37.792714	-122.248780	4898	Subscriber	1974.0	Male	Yes

	duration_sec	start_time	end_time	start_station_name	start_station_latitude	start_station_longitude	end_station_name	end_station_latitude	end_station_longitude	bike_id	...	start_time_hour	end_time_hour	start_time_day	end_time_day	start_time_weekday	end_time_weekday	start_time_month	end_time_month	member_age	distance_km
0	52185	2019-02-28 17:32:10.145	2019-03-01 08:01:55.975	Montgomery St BART Station (Market St at 2nd St)	37.789625	-122.400811	Commercial St at Montgomery St	37.794231	-122.402923	4902	...	17	8	28	1	Thursday	Friday	February	March	35	0.544709
1	42521	2019-02-28 18:53:21.789	2019-03-01 06:42:03.056	The Embarcadero at Steuart St	37.791464	-122.391034	Berry St at 4th St	37.775880	-122.393170	2535	...	18	6	28	1	Thursday	Friday	February	March	0	1.742999
2	61854	2019-02-28 12:13:13.218	2019-03-01 05:24:08.146	Market St at Dolores St	37.769305	-122.426826	Powell St BART Station (Market St at 4th St)	37.786375	-122.404904	5905	...	12	5	28	1	Thursday	Friday	February	March	47	2.704545
3	36490	2019-02-28 17:54:26.010	2019-03-01 04:02:36.842	Grove St at Masonic Ave	37.774836	-122.446546	Central Ave at Fell St	37.773311	-122.444293	6638	...	17	4	28	1	Thursday	Friday	February	March	30	0.260739
4	1585	2019-02-28 23:54:18.549	2019-03-01 00:20:44.074	Frank H Ogawa Plaza	37.804562	-122.271738	10th Ave at E 15th St	37.792714	-122.248780	4898	...	23	0	28	1	Thursday	Friday	February	March	45	2.409301
5	1793	2019-02-28 23:49:58.632	2019-03-01 00:19:51.760	4th St at Mission Bay Blvd S	37.770407	-122.391198	Broadway at Kearny	37.798014	-122.405950	5200	...	23	0	28	1	Thursday	Friday	February	March	60	3.332203
6	1147	2019-02-28 23:55:35.104	2019-03-01 00:14:42.588	Palm St at Willow St	37.317298	-121.884995	San Jose Diridon Station	37.329732	-121.901782	3803	...	23	0	28	1	Thursday	Friday	February	March	36	2.028551
7	1615	2019-02-28 23:41:06.766	2019-03-01 00:08:02.756	Washington St at Kearny St	37.795393	-122.404770	Valencia St at 21st St	37.756708	-122.421025	6329	...	23	0	28	1	Thursday	Friday	February	March	30	4.532568
8	1570	2019-02-28 23:41:48.790	2019-03-01 00:07:59.715	Washington St at Kearny St	37.795393	-122.404770	Valencia St at 21st St	37.756708	-122.421025	6548	...	23	0	28	1	Thursday	Friday	February	March	31	4.532568
9	1049	2019-02-28 23:49:47.699	2019-03-01 00:07:17.025	Post St at Kearny St	37.788975	-122.403452	Mission Playground	37.759210	-122.421339	6488	...	23	0	28	1	Thursday	Friday	February	March	27	3.664101
10	458	2019-02-28 23:57:57.211	2019-03-01 00:05:35.435	Jones St at Post St	37.787327	-122.413278	San Francisco Public Library (Grove St at Hyde...	37.778768	-122.415929	5318	...	23	0	28	1	Thursday	Friday	February	March	23	0.979824
11	506	2019-02-28 23:56:55.540	2019-03-01 00:05:21.733	Civic Center/UN Plaza BART Station (Market St ...	37.781074	-122.411738	Bryant St at 2nd St	37.783172	-122.393572	5848	...	23	0	28	1	Thursday	Friday	February	March	26	1.613447
12	1176	2019-02-28 23:45:12.651	2019-03-01 00:04:49.184	Valencia St at 21st St	37.756708	-122.421025	Broadway at Kearny	37.798014	-122.405950	5328	...	23	0	28	1	Thursday	Friday	February	March	29	4.780213
13	915	2019-02-28 23:49:06.062	2019-03-01 00:04:21.867	Channing Way at Shattuck Ave	37.865847	-122.267443	Shattuck Ave at Hearst Ave	37.873676	-122.268487	5101	...	23	0	28	1	Thursday	Friday	February	March	0	0.875424
14	395	2019-02-28 23:56:26.848	2019-03-01 00:03:01.947	Bancroft Way at College Ave	37.869360	-122.254337	Channing Way at Shattuck Ave	37.865847	-122.267443	4786	...	23	0	28	1	Thursday	Friday	February	March	31	1.214963
15	208	2019-02-28 23:59:18.548	2019-03-01 00:02:47.228	Howard St at Mary St	37.781010	-122.405666	8th St at Ringold St	37.774520	-122.409449	6361	...	23	0	28	1	Thursday	Friday	February	March	26	0.794502

	duration_sec	start_station_id	start_station_latitude	start_station_longitude	end_station_id	end_station_latitude	end_station_longitude	bike_id	member_birth_year
count	183412.000000	183215.000000	183412.000000	183412.000000	183215.000000	183412.000000	183412.000000	183412.000000	175147.000000
mean	726.078435	138.590427	37.771223	-122.352664	136.249123	37.771427	-122.352250	4472.906375	1984.806437
std	1794.389780	111.778864	0.099581	0.117097	111.515131	0.099490	0.116673	1664.383394	10.116689
min	61.000000	3.000000	37.317298	-122.453704	3.000000	37.317298	-122.453704	11.000000	1878.000000
25%	325.000000	47.000000	37.770083	-122.412408	44.000000	37.770407	-122.411726	3777.000000	1980.000000
50%	514.000000	104.000000	37.780760	-122.398285	100.000000	37.781010	-122.398279	4958.000000	1987.000000
75%	796.000000	239.000000	37.797280	-122.286533	235.000000	37.797320	-122.288045	5502.000000	1992.000000
max	85444.000000	398.000000	37.880222	-121.874119	398.000000	37.880222	-121.874119	6645.000000	2001.000000

	start_station_name	end_station_name
count	183412	183412
unique	330	330
top	Market St at 10th St	San Francisco Caltrain Station 2 (Townsend St...
freq	3904	4857

	count	mean	std	min	25%	50%	75%	max
user_type
Customer	16631.0	34.0	9.0	18.0	27.0	32.0	38.0	141.0
Subscriber	158516.0	34.0	10.0	18.0	27.0	32.0	39.0	119.0

	duration_sec	duration_minute	duration_hour
user_type
Customer	1432.465019	23.874411	0.397911
Subscriber	640.263678	10.671055	0.177846

	start_time_hour	end_time_hour	start_time_day	end_time_day
user_type
Customer	13.644806	13.911969	15.718643	15.718039
Subscriber	13.435779	13.578664	15.247994	15.247285

	distance_km	bike_id
user_type
Customer	1.867206	4225.550181
Subscriber	1.669211	4502.956226

	duration_sec	duration_minute	duration_hour
member_gender
Female	779.070390	12.984508	0.216419
Male	672.639069	11.210645	0.186836
Not_Defined	1189.462311	19.824347	0.330428
Other	996.515334	16.608535	0.276717

	start_time_hour	end_time_hour	start_time_day	end_time_day
member_gender
Female	13.216335	13.387474	15.378244	15.378440
Male	13.525277	13.674017	15.291976	15.291341
Not_Defined	13.503448	13.721355	14.964186	14.961887
Other	13.672234	13.796002	15.420591	15.411281

	distance_km	bike_id
member_gender
Female	1.765259	4396.909020
Male	1.662637	4507.239707
Not_Defined	1.718845	4274.845856
Other	1.795024	4542.819277

	duration_sec	duration_minute	duration_hour
bike_share_for_all_trip
No	730.454253	12.174229	0.202899
Yes	684.220174	11.403686	0.190069

	start_time_hour	end_time_hour	start_time_day	end_time_day
bike_share_for_all_trip
No	13.387587	13.546350	15.298357	15.297863
Yes	14.136010	14.269255	15.304914	15.302264

	distance_km	bike_id
bike_share_for_all_trip
No	1.731022	4482.769845
Yes	1.304558	4378.554237

	duration_sec	duration_minute	duration_hour
start_time_weekday
Friday	695.795073	11.596545	0.193272
Monday	713.159616	11.886002	0.198095
Saturday	902.661993	15.044383	0.250749
Sunday	919.746054	15.329088	0.255455
Thursday	699.040998	11.650676	0.194160
Tuesday	663.305567	11.055108	0.184277
Wednesday	673.671165	11.227834	0.187119

	start_time_hour	end_time_hour	start_time_day	end_time_day
start_time_weekday
Friday	12.823022	12.971671	12.725199	12.727097
Monday	13.395241	13.552585	14.707508	14.708997
Saturday	13.652126	13.826550	14.300097	14.303148
Sunday	14.178058	14.360562	14.679894	14.682922
Thursday	13.620706	13.773731	17.969657	17.958491
Tuesday	13.229717	13.383711	14.586521	14.587559
Wednesday	13.712122	13.858473	16.787996	16.789447

	duration_sec	duration_minute	duration_hour
net_age_groups
0-20	724.248113	12.070729	0.201191
20-30	707.930257	11.798826	0.196635
30-40	687.884419	11.464745	0.191076
40-50	713.497823	11.891640	0.198202
50-60	736.022779	12.267001	0.204435
60-70	764.203723	12.736785	0.212274
70-140	626.126957	10.435287	0.174052

	start_time_hour	end_time_hour	start_time_day	end_time_day
net_age_groups
0-20	14.376415	14.508962	14.945519	14.948349
20-30	13.745989	13.894302	15.409927	15.409057
30-40	13.309411	13.462214	15.259697	15.259290
40-50	13.104036	13.266984	15.224127	15.222993
50-60	13.016952	13.185326	15.258167	15.257108
60-70	12.874112	13.059222	15.392893	15.393909
70-140	12.820870	13.003478	16.744348	16.744348

	distance_km	bike_id
net_age_groups
0-20	1.299768	4403.791509
20-30	1.654897	4517.431520
30-40	1.767911	4520.030210
40-50	1.687907	4409.832245
50-60	1.642379	4331.067544
60-70	1.601229	4090.001015
70-140	1.461706	4344.766957

Analyzing and Visualizing Ford GoBike System Data (February 2019)¶

presented by Sherif Elghazawy¶

Table of Contents¶

Introduction¶

Investigation Overview¶

Importing Data and Libararies¶

Dataset Overview¶

Wrangling Data¶

First, let's start assessing our dataset.¶

Insight:¶

Insight:¶

Now, let's start cleaning our dataset:¶

Wrangling Data Summary:¶

Exploratory Analysis and Visualization¶

What is/are the main feature(s) of interest in your dataset?¶

What features in the dataset do you think will help support your investigation into your feature(s) of interest?¶

Now, let's srart our exploration and visualization journey¶

Univariate Exploration¶

1. Ride Duration¶

1.1 Duration in seconds:¶

Insights:¶

Insights:¶

1.2 Duration in minutes¶

Insights:¶

Insights:¶

1.3 Duration in hours¶

Insights:¶

Insights:¶

2. Ride Start and End Times¶

2.1 Start Time Hour and End Time Hour¶

Insights:¶

Insights:¶

2.3 Start Time Day and End Time Day¶

Insights:¶

Insights:¶

Insights:¶

Insights:¶

2.4 Start Time Weekday and End Time Weekday¶

Insights:¶

Insights:¶

2.5 Start Time Month and End Time Month¶

Insights:¶

3. Ride Stations and Distance¶

3.1 Ride Stations¶

Insights:¶

Insight:¶

Insights:¶

3.2 Ride Distance¶

Insights:¶

Insights:¶

4. Bike Id and Bike Share¶

4.1 Bike Id¶

Insights:¶

4.2 Bike Share¶

Insights:¶

5.User Characteristics¶

5.1 User Type¶

Insights:¶

5.2 Member Gender¶

Insights:¶

5.3 Member Age¶

Insights:¶

Insights:¶

Insights:¶

Insights:¶

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?¶

Duration Variables:¶

Start and End Time Variables:¶

Ride Distance(km) Variable:¶

Member Age variable:¶

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?¶

Duration Variables:¶

Start and End Time Variables:¶

Ride Distance(km) Variable:¶

Member Age variable:¶

Bivariate Exploration¶

1. Categorical Variable vs. Categorical Variable¶

1.1 User Type vs. Member Gender & User Type vs. Bike Share (2 plots)¶

Insights:¶

1.2 User Type vs. Age Groups (1 plot)¶